Interest in Low-Threshold Employment among People who Inject Illicit Drugs: Implications for Street Disorder

PubMed Central

DeBeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2011-01-01

Background Income generation opportunities available to people who use illicit drugs have been associated with street disorder. Among a cohort of injection drug users (IDU) we sought to examine street-based income generation practices and willingness to forgo these sources of income if other low-threshold work opportunities were made available. Methods Data were derived from a prospective community recruited cohort of IDU. We assessed the prevalence of engaging in disorderly street-based income generation activities, including sex work, drug dealing, panhandling, and recycling/salvaging/vending. Using multivariate logistic regressions based on Akaike information criterion and the best subset selection procedure, we identified factors associated with disorderly income generation activities, and assessed willingness to forgo these sources of income during the period of November 2008 to July 2009. Results Among our sample of 874 IDU, 418 (48%) reported engaging in a disorderly income generation activity in the previous six months. In multivariate analyses, engaging in disorderly income generation activities was independently associated with high intensity stimulant use, as well as binge drug use, having encounters with police, being a victim of violence, sharing used syringes, and injecting in public areas. Among those engaged in disorderly income generation, 198 (47%) reported a willingness to forgo these income sources if given opportunities for low-threshold employment, with sex workers being most willing to engage in alternative employment. Conclusion Engagement in disorderly street-based income generation activities was associated with high intensity stimulant drug use and various markers of risk. We found that a high proportion of illicit drug users were willing to cease engagement in these activities if they had options for causal low-threshold employment. These findings indicate that there is a high demand for low-threshold employment that may offer important opportunities to reduce drug-related street disorder and associated harms. PMID:21684142

Interest in low-threshold employment among people who inject illicit drugs: implications for street disorder.

PubMed

Debeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2011-09-01

Income generation opportunities available to people who use illicit drugs have been associated with street disorder. Among a cohort of injection drug users (IDU) we sought to examine street-based income generation practices and willingness to forgo these sources of income if other low-threshold work opportunities were made available. Data were derived from a prospective community recruited cohort of IDU. We assessed the prevalence of engaging in disorderly street-based income generation activities, including sex work, drug dealing, panhandling, and recycling/salvaging/vending. Using multivariate logistic regressions based on Akaike information criterion and the best subset selection procedure, we identified factors associated with disorderly income generation activities, and assessed willingness to forgo these sources of income during the period of November 2008 to July 2009. Among our sample of 874 IDU, 418 (48%) reported engaging in a disorderly income generation activity in the previous six months. In multivariate analyses, engaging in disorderly income generation activities was independently associated with high intensity stimulant use, as well as binge drug use, having encounters with police, being a victim of violence, sharing used syringes, and injecting in public areas. Among those engaged in disorderly income generation, 198 (47%) reported a willingness to forgo these income sources if given opportunities for low-threshold employment, with sex workers being most willing to engage in alternative employment. Engagement in disorderly street-based income generation activities was associated with high intensity stimulant drug use and various markers of risk. We found that a high proportion of illicit drug users were willing to cease engagement in these activities if they had options for causal low-threshold employment. These findings indicate that there is a high demand for low-threshold employment that may offer important opportunities to reduce drug-related street disorder and associated harms. Copyright © 2011 Elsevier B.V. All rights reserved.

Influence of aging on thermal and vibratory thresholds of quantitative sensory testing.

PubMed

Lin, Yea-Huey; Hsieh, Song-Chou; Chao, Chi-Chao; Chang, Yang-Chyuan; Hsieh, Sung-Tsang

2005-09-01

Quantitative sensory testing has become a common approach to evaluate thermal and vibratory thresholds in various types of neuropathies. To understand the effect of aging on sensory perception, we measured warm, cold, and vibratory thresholds by performing quantitative sensory testing on a population of 484 normal subjects (175 males and 309 females), aged 48.61 +/- 14.10 (range 20-86) years. Sensory thresholds of the hand and foot were measured with two algorithms: the method of limits (Limits) and the method of level (Level). Thresholds measured by Limits are reaction-time-dependent, while those measured by Level are independent of reaction time. In addition, we explored (1) the correlations of thresholds between these two algorithms, (2) the effect of age on differences in thresholds between algorithms, and (3) differences in sensory thresholds between the two test sites. Age was consistently and significantly correlated with sensory thresholds of all tested modalities measured by both algorithms on multivariate regression analysis compared with other factors, including gender, body height, body weight, and body mass index. When thresholds were plotted against age, slopes differed between sensory thresholds of the hand and those of the foot: for the foot, slopes were steeper compared with those for the hand for each sensory modality. Sensory thresholds of both test sites measured by Level were highly correlated with those measured by Limits, and thresholds measured by Limits were higher than those measured by Level. Differences in sensory thresholds between the two algorithms were also correlated with age: thresholds of the foot were higher than those of the hand for each sensory modality. This difference in thresholds (measured with both Level and Limits) between the hand and foot was also correlated with age. These findings suggest that age is the most significant factor in determining sensory thresholds compared with the other factors of gender and anthropometric parameters, and this provides a foundation for investigating the neurobiologic significance of aging on the processing of sensory stimuli.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Concentration-Dependent Antagonism and Culture Conversion in Pulmonary Tuberculosis

PubMed Central

Pasipanodya, Jotam G.; Denti, Paolo; Sirgel, Frederick; Lesosky, Maia; Gumbo, Tawanda; Meintjes, Graeme; McIlleron, Helen; Wilkinson, Robert J.

2017-01-01

Abstract Background. There is scant evidence to support target drug exposures for optimal tuberculosis outcomes. We therefore assessed whether pharmacokinetic/pharmacodynamic (PK/PD) parameters could predict 2-month culture conversion. Methods. One hundred patients with pulmonary tuberculosis (65% human immunodeficiency virus coinfected) were intensively sampled to determine rifampicin, isoniazid, and pyrazinamide plasma concentrations after 7–8 weeks of therapy, and PK parameters determined using nonlinear mixed-effects models. Detailed clinical data and sputum for culture were collected at baseline, 2 months, and 5–6 months. Minimum inhibitory concentrations (MICs) were determined on baseline isolates. Multivariate logistic regression and the assumption-free multivariate adaptive regression splines (MARS) were used to identify clinical and PK/PD predictors of 2-month culture conversion. Potential PK/PD predictors included 0- to 24-hour area under the curve (AUC0-24), maximum concentration (Cmax), AUC0-24/MIC, Cmax/MIC, and percentage of time that concentrations persisted above the MIC (%TMIC). Results. Twenty-six percent of patients had Cmax of rifampicin <8 mg/L, pyrazinamide <35 mg/L, and isoniazid <3 mg/L. No relationship was found between PK exposures and 2-month culture conversion using multivariate logistic regression after adjusting for MIC. However, MARS identified negative interactions between isoniazid Cmax and rifampicin Cmax/MIC ratio on 2-month culture conversion. If isoniazid Cmax was <4.6 mg/L and rifampicin Cmax/MIC <28, the isoniazid concentration had an antagonistic effect on culture conversion. For patients with isoniazid Cmax >4.6 mg/L, higher isoniazid exposures were associated with improved rates of culture conversion. Conclusions. PK/PD analyses using MARS identified isoniazid Cmax and rifampicin Cmax/MIC thresholds below which there is concentration-dependent antagonism that reduces 2-month sputum culture conversion. PMID:28205671

Emergency department blood transfusion: the first two units are free.

PubMed

Ley, Eric J; Liou, Douglas Z; Singer, Matthew B; Mirocha, James; Melo, Nicolas; Chung, Rex; Bukur, Marko; Salim, Ali

2013-09-01

Studies on blood product transfusions after trauma recommend targeting specific ratios to reduce mortality. Although crystalloid volumes as little as 1.5 L predict increased mortality after trauma, little data is available regarding the threshold of red blood cell (RBC) transfusion volume that predicts increased mortality. Data from a level I trauma center between January 2000 and December 2008 were reviewed. Trauma patients who received at least 100 mL RBC in the emergency department (ED) were included. Each unit of RBC was defined as 300 mL. Demographics, RBC transfusion volume, and mortality were analyzed in the nonelderly (<70 y) and elderly (≥70 y). Multivariate logistic regression was performed at various volume cutoffs to determine whether there was a threshold transfusion volume that independently predicted mortality. A total of 560 patients received ≥100 mL RBC in the ED. Overall mortality was 24.3%, with 22.5% (104 deaths) in the nonelderly and 32.7% (32 deaths) in the elderly. Multivariate logistic regression demonstrated that RBC transfusion of ≥900 mL was associated with increased mortality in both the nonelderly (adjusted odds ratio 2.06, P = 0.008) and elderly (adjusted odds ratio 5.08, P = 0.006). Although transfusion of greater than 2 units in the ED was an independent predictor of mortality, transfusion of 2 units or less was not. Interestingly, unlike crystalloid volume, stepwise increases in blood volume were not associated with stepwise increases in mortality. The underlying etiology for mortality discrepancies, such as transfusion ratios, hypothermia, or immunosuppression, needs to be better delineated. Copyright © 2013 Elsevier Inc. All rights reserved.

Pulp Sensitivity: Influence of Sex, Psychosocial Variables, COMT Gene, and Chronic Facial Pain.

PubMed

Mladenovic, Irena; Krunic, Jelena; Supic, Gordana; Kozomara, Ruzica; Bokonjic, Dejan; Stojanovic, Nikola; Magic, Zvonko

2018-05-01

The purpose of this study was to evaluate the associations of variability in pulp sensitivity with sex, psychosocial variables, the gene that encodes for the enzyme catechol-O-methyltransferase (COMT), and chronic painful conditions (temporomandibular disorders [TMDs]). The study was composed of 97 subjects (68 women and 29 men aged 20-44 years). The electric (electric pulp tester) and cold (refrigerant spray) stimuli were performed on mandibular lateral incisors. The results were expressed as pain threshold values for electric pulp stimulation (0-80 units) and as pain intensity scores (visual numeric scale from 0-10) for cold stimulation. The Research Diagnostic Criteria for TMD were used to assess TMD, depression, and somatization. DNA extracted from peripheral blood was genotyped for 3 COMT polymorphisms (rs4680, rs6269, and rs165774) using the real-time TaqMan method. Multivariate linear regression was used to investigate the joint effect of the predictor variables (clinical and genetic) on pulp sensitivity (dependent variables). Threshold responses to electric stimuli were related to female sex (P < .01) and the homozygous GG genotype for the rs165774 polymorphism (P < .05). Pain intensity to cold stimuli was higher in TMD patients (P < .01) and tended to be higher in women. Multivariate linear regression identified sex and the rs165774 COMT polymorphism as the determinants of electric pain sensitivity, whereas TMD accounts for the variability in the cold response. Our findings indicate that sex/a COMT gene variant and TMD as a chronic painful condition may contribute to individual variation in electric and cold pulp sensitivity, respectively. Copyright © 2018 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Risk of radiation pneumonitis in patients receiving taxane-based trimodality therapy for locally advanced esophageal cancer.

PubMed

Shaikh, Talha; Churilla, Thomas M; Monpara, Pooja; Scott, Walter J; Cohen, Steven J; Meyer, Joshua E

There are limited data regarding clinical and treatment factors associated with radiation pneumonitis (RP) in patients receiving taxane-based trimodality therapy for esophageal cancer. The purpose of this study was to identify predictors of RP in patients undergoing trimodality therapy. We retrospectively reviewed patients undergoing chemoradiation followed by esophagectomy between 2006 and 2011. The association between clinical and dosimetric factors with RP was assessed using χ 2 test and Mann-Whitney U test. Multivariable regression was used to assess the relationship between grade 2+ RP and clinical/dosimetric factors. Receiver operator curves were generated to identify threshold doses for RP. A total of 139 patients were included; 19 (13.7%) patients experienced grade 2+ RP. Patients with upper/middle thoracic tumors (P = .038) and receiving higher radiation doses (P = .038) were more likely to develop grade 2+ RP. There was no association between taxane-based therapy and grade 2+ RP (P = .728). The percent volume of lung receiving 5 Gy (V5; P < .001), 10 Gy (P < .001), 20 Gy (V20; P < .001), and 30 Gy (P < .001) was associated with an increased risk of grade 2+ RP. On multivariable regression, the lung V5 (odds ratio, 1.101; 95% confidence interval, 1.1014-1.195) and V20 (odds ratio, 1.149; 95% confidence interval, 1.1015-1.301) remained associated with grade 2+ RP. A V5 ≤65% and V20 ≤25% were identified as optimal thresholds for increased grade 2+ RP. Dosimetric parameters are strong predictors of symptomatic RP in patients undergoing trimodality therapy for esophageal cancer. Mitigating the risk of RP in these patients should be an important consideration during treatment planning. Copyright © 2016 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Characteristics of Fibromyalgia Independently Predict Poorer Long‐Term Analgesic Outcomes Following Total Knee and Hip Arthroplasty

PubMed Central

Urquhart, Andrew G.; Hassett, Afton L.; Tsodikov, Alex; Hallstrom, Brian R.; Wood, Nathan I.; Williams, David A.; Clauw, Daniel J.

2015-01-01

Objective While psychosocial factors have been associated with poorer outcomes after knee and hip arthroplasty, we hypothesized that augmented pain perception, as occurs in conditions such as fibromyalgia, may account for decreased responsiveness to primary knee and hip arthroplasty. Methods A prospective, observational cohort study was conducted. Preoperative phenotyping was conducted using validated questionnaires to assess pain, function, depression, anxiety, and catastrophizing. Participants also completed the 2011 fibromyalgia survey questionnaire, which addresses the widespread body pain and comorbid symptoms associated with characteristics of fibromyalgia. Results Of the 665 participants, 464 were retained 6 months after surgery. Since individuals who met criteria for being classified as having fibromyalgia were expected to respond less favorably, all primary analyses excluded these individuals (6% of the cohort). In the multivariate linear regression model predicting change in knee/hip pain (primary outcome), a higher fibromyalgia survey score was independently predictive of less improvement in pain (estimate −0.25, SE 0.044; P < 0.00001). Lower baseline joint pain scores and knee (versus hip) arthroplasty were also predictive of less improvement (R2 = 0.58). The same covariates were predictive in the multivariate logistic regression model for change in knee/hip pain, with a 17.8% increase in the odds of failure to meet the threshold of 50% improvement for every 1‐point increase in fibromyalgia survey score (P = 0.00032). The fibromyalgia survey score was also independently predictive of change in overall pain and patient global impression of change. Conclusion Our findings indicate that the fibromyalgia survey score is a robust predictor of poorer arthroplasty outcomes, even among individuals whose score falls well below the threshold for the categorical diagnosis of fibromyalgia. PMID:25772388

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

Comparison of Various Anthropometric Indices as Risk Factors for Hearing Impairment in Asian Women

PubMed Central

Lee, Kyu Yup; Choi, Eun Woo; Do, Jun Young

2015-01-01

Background The objective of the present study was to examine the associations between various anthropometric measures and metabolic syndrome and hearing impairment in Asian women. Methods We identified 11,755 women who underwent voluntary routine health checkups at Yeungnam University Hospital between June 2008 and April 2014. Among these patients, 2,485 participants were <40 years old, and 1,072 participants lacked information regarding their laboratory findings or hearing and were therefore excluded. In total 8,198 participants were recruited into our study. Results The AUROC value for metabolic syndrome was 0.790 for the waist to hip ratio (WHR). The cutoff value was 0.939. The sensitivity and specificity for predicting metabolic syndrome were 72.7% and 71.7%, respectively. The AUROC value for hearing loss was 0.758 for WHR. The cutoff value was 0.932. The sensitivity and specificity for predicting hearing loss were 65.8% and 73.4%, respectively. The WHR had the highest AUC and was the best predictor of metabolic syndrome and hearing loss. Univariate and multivariate linear regression analyses showed that WHR levels were positively associated with four hearing thresholds including averaged hearing threshold and low, middle, and high frequency thresholds. In addition, multivariate logistic analysis revealed that those with a high WHR had a 1.347–fold increased risk of hearing loss compared with the participants with a low WHR. Conclusion Our results demonstrated that WHR may be a surrogate marker for predicting the risk of hearing loss resulting from metabolic syndrome. PMID:26575369

Corneal Mechanical Thresholds Negatively Associate With Dry Eye and Ocular Pain Symptoms.

PubMed

Spierer, Oriel; Felix, Elizabeth R; McClellan, Allison L; Parel, Jean Marie; Gonzalez, Alex; Feuer, William J; Sarantopoulos, Constantine D; Levitt, Roy C; Ehrmann, Klaus; Galor, Anat

2016-02-01

To examine associations between corneal mechanical thresholds and metrics of dry eye. This was a cross-sectional study of individuals seen in the Miami Veterans Affairs eye clinic. The evaluation consisted of questionnaires regarding dry eye symptoms and ocular pain, corneal mechanical detection and pain thresholds, and a comprehensive ocular surface examination. The main outcome measures were correlations between corneal thresholds and signs and symptoms of dry eye and ocular pain. A total of 129 subjects participated in the study (mean age 64 ± 10 years). Mechanical detection and pain thresholds on the cornea correlated with age (Spearman's ρ = 0.26, 0.23, respectively; both P < 0.05), implying decreased corneal sensitivity with age. Dry eye symptom severity scores and Neuropathic Pain Symptom Inventory (modified for the eye) scores negatively correlated with corneal detection and pain thresholds (range, r = -0.13 to -0.27, P < 0.05 for values between -0.18 and -0.27), suggesting increased corneal sensitivity in those with more severe ocular complaints. Ocular signs, on the other hand, correlated poorly and nonsignificantly with mechanical detection and pain thresholds on the cornea. A multivariable linear regression model found that both posttraumatic stress disorder (PTSD) score (β = 0.21, SE = 0.03) and corneal pain threshold (β = -0.03, SE = 0.01) were significantly associated with self-reported evoked eye pain (pain to wind, light, temperature) and explained approximately 32% of measurement variability (R = 0.57). Mechanical detection and pain thresholds measured on the cornea are correlated with dry eye symptoms and ocular pain. This suggests hypersensitivity within the corneal somatosensory pathways in patients with greater dry eye and ocular pain complaints.

Corneal Mechanical Thresholds Negatively Associate With Dry Eye and Ocular Pain Symptoms

PubMed Central

Spierer, Oriel; Felix, Elizabeth R.; McClellan, Allison L.; Parel, Jean Marie; Gonzalez, Alex; Feuer, William J.; Sarantopoulos, Constantine D.; Levitt, Roy C.; Ehrmann, Klaus; Galor, Anat

2016-01-01

Purpose To examine associations between corneal mechanical thresholds and metrics of dry eye. Methods This was a cross-sectional study of individuals seen in the Miami Veterans Affairs eye clinic. The evaluation consisted of questionnaires regarding dry eye symptoms and ocular pain, corneal mechanical detection and pain thresholds, and a comprehensive ocular surface examination. The main outcome measures were correlations between corneal thresholds and signs and symptoms of dry eye and ocular pain. Results A total of 129 subjects participated in the study (mean age 64 ± 10 years). Mechanical detection and pain thresholds on the cornea correlated with age (Spearman's ρ = 0.26, 0.23, respectively; both P < 0.05), implying decreased corneal sensitivity with age. Dry eye symptom severity scores and Neuropathic Pain Symptom Inventory (modified for the eye) scores negatively correlated with corneal detection and pain thresholds (range, r = −0.13 to −0.27, P < 0.05 for values between −0.18 and −0.27), suggesting increased corneal sensitivity in those with more severe ocular complaints. Ocular signs, on the other hand, correlated poorly and nonsignificantly with mechanical detection and pain thresholds on the cornea. A multivariable linear regression model found that both posttraumatic stress disorder (PTSD) score (β = 0.21, SE = 0.03) and corneal pain threshold (β = −0.03, SE = 0.01) were significantly associated with self-reported evoked eye pain (pain to wind, light, temperature) and explained approximately 32% of measurement variability (R = 0.57). Conclusions Mechanical detection and pain thresholds measured on the cornea are correlated with dry eye symptoms and ocular pain. This suggests hypersensitivity within the corneal somatosensory pathways in patients with greater dry eye and ocular pain complaints. PMID:26886896

Explanatory factors and predictors of fatigue in persons with rheumatoid arthritis: A longitudinal study.

PubMed

Feldthusen, Caroline; Grimby-Ekman, Anna; Forsblad-d'Elia, Helena; Jacobsson, Lennart; Mannerkorpi, Kaisa

2016-04-28

To investigate the impact of disease-related aspects on long-term variations in fatigue in persons with rheumatoid arthritis. Observational longitudinal study. Sixty-five persons with rheumatoid arthritis, age range 20-65 years, were invited to a clinical examination at 4 time-points during the 4 seasons. Outcome measures were: general fatigue rated on visual analogue scale (0-100) and aspects of fatigue assessed by the Bristol Rheumatoid Arthritis Fatigue Multidimensional Questionnaire. Disease-related variables were: disease activity (erythrocyte sedimentation rate), pain threshold (pressure algometer), physical capacity (six-minute walk test), pain (visual analogue scale (0-100)), depressive mood (Hospital Anxiety and Depression scale, depression subscale), personal factors (age, sex, body mass index) and season. Multivariable regression analysis, linear mixed effects models were applied. The strongest explanatory factors for all fatigue outcomes, when recorded at the same time-point as fatigue, were pain threshold and depressive mood. Self-reported pain was an explanatory factor for physical aspects of fatigue and body mass index contributed to explaining the consequences of fatigue on everyday living. For predicting later fatigue pain threshold and depressive mood were the strongest predictors. Pain threshold and depressive mood were the most important factors for fatigue in persons with rheumatoid arthritis.

Penalized spline estimation for functional coefficient regression models.

PubMed

Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan

2010-04-01

The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.

Regression Model Optimization for the Analysis of Experimental Data

NASA Technical Reports Server (NTRS)

Ulbrich, N.

2009-01-01

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

Utility of respiratory ward-based NIV in acidotic hypercapnic respiratory failure.

PubMed

Dave, Chirag; Turner, Alice; Thomas, Ajit; Beauchamp, Ben; Chakraborty, Biman; Ali, Asad; Mukherjee, Rahul; Banerjee, Dev

2014-11-01

We sought to elicit predictors of in-hospital mortality for first and subsequent admissions with acidotic hypercapnic respiratory failure (AHRF) in a cohort of chronic obstructive pulmonary disease patients who have undergone ward-based non-invasive ventilation (NIV), and identify features associated with long-term survival. Analysis of prospectively collected data at a single centre on patients undergoing NIV for AHRF between 2004 and 2009. Predictors of in-hospital mortality and intubation were sought by logistic regression and predictors of long-term survival by Cox regression. Initial pH exhibited a threshold effect for in-hospital mortality at pH 7.15. This relationship remained in patients undergoing their first episode of AHRF. In both first and subsequent admissions, a pH threshold of 7.25 at 4 h was associated with better prognosis (P = 0.02 and P = 0.04 respectively). In second or subsequent episodes of AHRF, mortality was lower and predicted only by age (P = 0.002) on multivariate analysis. NIV could be used on medical wards for patients with pH 7.16 or greater on their first admission, although more conservative values should continue to be used for those with a second or subsequent episodes of AHRF. © 2014 Asian Pacific Society of Respirology.

Adjustment of geochemical background by robust multivariate statistics

USGS Publications Warehouse

Zhou, D.

1985-01-01

Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.

Speed of response in ultrabrief and brief pulse width right unilateral ECT.

PubMed

Loo, Colleen K; Garfield, Joshua B B; Katalinic, Natalie; Schweitzer, Isaac; Hadzi-Pavlovic, Dusan

2013-05-01

Ultrabrief pulse width stimulation electroconvulsive therapy (ECT) results in less cognitive side-effects than brief pulse ECT, but recent work suggests that more treatment sessions may be required to achieve similar efficacy. In this retrospective analysis of subjects pooled from three research studies, time to improvement was analysed in 150 depressed subjects who received right unilateral ECT with a brief pulse width (at five times seizure threshold) or ultrabrief pulse width (at six times seizure threshold). Multivariate Cox regression analyses compared the number of treatments required for 50% reduction in depression scores (i.e. speed of response) in these two samples. The analyses controlled for clinical, demographic and treatment variables that differed between the samples or that were found to be significant predictors of speed of response in univariate analyses. In the multivariate analysis, older age predicted faster speed of response. There was a non-significant trend for faster time to 50% improvement with brief pulse ECT (p = 0.067). Remission rates were higher after brief pulse ECT than ultrabrief pulse ECT (p = 0.007) but response rates were similar. This study, the largest of its kind reported to date, suggests that fewer treatments may be needed to attain response with brief than ultrabrief pulse ECT and that remission rates are higher with brief pulse ECT. Further research with a larger randomized and blinded study is recommended.

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss treated with adjuvant hyperbaric oxygen therapy.

PubMed

Xie, Shaobing; Qiang, Qingfen; Mei, Lingyun; He, Chufeng; Feng, Yong; Sun, Hong; Wu, Xuewen

2018-01-01

The objective of this study is to evaluate possible prognostic factors of idiopathic sudden sensorineural hearing loss (ISSNHL) treated with adjuvant hyperbaric oxygen therapy (HBOT) using univariate and multivariate analyses. From January 2008 to October 2016, records of 178 ISSNHL patients treated with auxiliary hyperbaric oxygen therapy were reviewed to assess hearing recovery and evaluate associated prognostic factors (gender, age, localization, initial hearing threshold, presence of tinnitus, vertigo, ear fullness, hypertension, diabetes, onset of HBOT, number of HBOT, and audiogram), by using univariate and multivariate analyses. The overall recovery rate was 37.1%, including complete recovery (19.7%) and partial recovery (17.4%). According to multivariate analysis, later onset of HBOT and higher initial hearing threshold were associated with a poor prognosis in ISSNHL patients treated with HBOT. HBOT is a safe and beneficial adjuvant therapy for ISSNHL patients. 20 sessions of HBOT is possibly enough to show its therapeutic effect. Earlier HBOT onset and lower initial hearing threshold is associated with favorable hearing recovery.

Long-term follow-up after near-infrared spectroscopy coronary imaging: Insights from the lipid cORe plaque association with CLinical events (ORACLE-NIRS) registry.

PubMed

Danek, Barbara Anna; Karatasakis, Aris; Karacsonyi, Judit; Alame, Aya; Resendes, Erica; Kalsaria, Pratik; Nguyen-Trong, Phuong-Khanh J; Rangan, Bavana V; Roesle, Michele; Abdullah, Shuaib; Banerjee, Subhash; Brilakis, Emmanouil S

Coronary lipid core plaque may be associated with the incidence of subsequent cardiovascular events. We analyzed outcomes of 239 patients who underwent near-infrared spectroscopy (NIRS) coronary imaging between 2009-2011. Multivariable Cox regression was used to identify variables independently associated with the incidence of major adverse cardiovascular events (MACE; cardiac mortality, acute coronary syndromes (ACS), stroke, and unplanned revascularization) during follow-up. Mean patient age was 64±9years, 99% were men, and 50% were diabetic, presenting with stable coronary artery disease (61%) or an acute coronary syndrome (ACS, 39%). Target vessel pre-stenting median lipid core burden index (LCBI) was 88 [interquartile range, IQR 50-130]. Median LCBI in non-target vessels was 57 [IQR 26-94]. Median follow-up was 5.3years. The 5-year MACE rate was 37.5% (cardiac mortality was 15.0%). On multivariable analysis the following variables were associated with MACE: diabetes mellitus, prior percutaneous coronary intervention performed at index angiography, and non-target vessel LCBI. Non-target vessel LCBI of 77 was determined using receiver-operating characteristic curve analysis to be a threshold for prediction of MACE in our cohort. The adjusted hazard ratio (HR) for non-target vessel LCBI ≥77 was 14.05 (95% confidence interval (CI) 2.47-133.51, p=0.002). The 5-year cumulative incidence of events in the above-threshold group was 58.0% vs. 13.1% in the below-threshold group. During long-term follow-up of patients who underwent NIRS imaging, high LCBI in a non-PCI target vessel was associated with increased incidence of MACE. Published by Elsevier Inc.

Impact of hyperglycemia on outcomes of patients with Pseudomonas aeruginosa bacteremia.

PubMed

Patel, Twisha S; Cottreau, Jessica M; Hirsch, Elizabeth B; Tam, Vincent H

2016-02-01

Bacteremia caused by Pseudomonas aeruginosa is associated with significant morbidity and mortality. In other bacterial infections, hyperglycemia has been identified as a risk factor for mortality in nondiabetic patients. The objective of this study was to determine the impact of early hyperglycemia on outcomes in diabetic and nondiabetic patients with P. aeruginosa bacteremia. A retrospective cohort study was performed in adult patients (≥18 years old) with P. aeruginosa bacteremia. Patients received at least 1 drug empirically to which the isolate was susceptible in vitro. Classification and regression tree analysis was used to determine the threshold breakpoint for average blood glucose concentration within 48 hours of positive blood culture (BG48). Logistic regression was used to explore independent risk factors for 30-day mortality. A total of 176 bacteremia episodes were identified; patients in 66 episodes were diabetic. Diabetic patients had higher BG48 (165.2±64.8 mg/dL versus 123.7±31.5 mg/dL, P<0.001) and lower 30-day mortality (10.7% versus 22.7%, P=0.046) than nondiabetic patients. Multivariate regression revealed 30-day mortality in nondiabetic patients was associated with Acute Physiology and Chronic Health Evaluation II score (odds ratio [OR] 1.1; 95% confidence interval [CI] 1.0-1.2) and BG48 >168 mg/dL (OR 6.3; 95% CI 1.7-23.3). However, blood glucose concentration was not identified as an independent risk factor for mortality in diabetic patients by multivariate regression analysis. Hyperglycemia did not appear to affect outcomes in diabetic patients, whereas nondiabetic patients had a higher risk of mortality from P. aeruginosa bacteremia. Prospective studies evaluating the impact of glycemic control in these patients are needed. Copyright © 2016 Elsevier Inc. All rights reserved.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pain threshold, tolerance and intensity in adolescents born very preterm or with low birth weight.

PubMed

van Ganzewinkel, Christ-Jan J L M; Been, Jasper V; Verbeek, Inge; van der Loo, Tera Boelen; van der Pal, Sylvia M; Kramer, Boris W; Andriessen, Peter

2017-07-01

Data on long-term consequences of neonatal pain is limited. To assess whether perinatal factors, later pain experience and pain coping strategies are associated with altered pain threshold, pain tolerance and pain intensity in adolescents born preterm. Observational, longitudinal study (Project on Preterm and SGA-infants, POPS-19). We analyzed data of 412 adolescents at the age of 19years, who were born at a gestational age<32weeks or with a birth weight<1500g. Participants performed a standardized cold pressor test to assess pain threshold, tolerance and intensity. Furthermore, they completed a pain coping questionnaire (PCQ). In univariate analysis, female gender and necrotizing enterocolitis (NEC) were associated with lower pain tolerance, indicated by reaching the ceiling time of 180s in ice water (females 19% vs males 29%, NEC 7% vs no NEC 25%). Female gender was associated with higher pain intensity (mean difference 0.58; 95%CI 0.21; 0.95) and lower pain threshold (log rank test p 0.007). In a multivariate Cox regression analyses, emotion focused avoidance pain coping style was significantly associated with lower pain threshold (hazard ratio HR 1.38; 95%CI 1.02; 1.87) and pain tolerance (HR 1.72; 95%CI 1.21; 2.42). NEC was significantly associated with lower pain threshold (HR 1.47; 95%CI 1.01; 2.14) and pain tolerance (HR 1.63; 95%CI 1.09; 2.41). In adolescence, maladaptive pain coping strategy was associated with lower pain threshold, pain tolerance and higher pain intensity. NEC was associated with altered pain response in adolescents born preterm. Copyright © 2017 Elsevier B.V. All rights reserved.

Low Oxygen Delivery as a Predictor of Acute Kidney Injury during Cardiopulmonary Bypass.

PubMed

Newland, Richard F; Baker, Robert A

2017-12-01

Low indexed oxygen delivery (DO 2 i) during cardiopulmonary bypass (CPB) has been associated with an increase in the likelihood of acute kidney injury (AKI), with critical thresholds for oxygen delivery reported to be 260-270 mL/min/m 2 . This study aims to explore whether a relationship exists for oxygen delivery during CPB, in which the integral of amount and time below a critical threshold, is associated with the incidence of postoperative AKI. The area under the curve (AUC) with DO 2 i during CPB above or below 270 mL/min/m 2 was calculated as a metric of oxygen delivery in 210 patients undergoing CPB. To determine the influence of low oxygen delivery on AKI, a multivariate logistic regression model was developed including AUC < 0, Euroscore II to provide preoperative risk factor adjustment, and incidence of red blood cell transfusion to adjust for the influence of transfusion. Having an AUC < 0 for an oxygen delivery threshold of 270 mL/min/m 2 during CPB was an independent predictor of AKI, after adjustment for Euroscore II and transfusion [OR 2.74, CI {1.01-7.41}, p = .047]. These results support that a relationship exists for oxygen delivery during CPB, in which the integral of amount and time below a critical threshold is associated with the incidence of postoperative AKI.

Regression Discontinuity for Causal Effect Estimation in Epidemiology.

PubMed

Oldenburg, Catherine E; Moscoe, Ellen; Bärnighausen, Till

Regression discontinuity analyses can generate estimates of the causal effects of an exposure when a continuously measured variable is used to assign the exposure to individuals based on a threshold rule. Individuals just above the threshold are expected to be similar in their distribution of measured and unmeasured baseline covariates to individuals just below the threshold, resulting in exchangeability. At the threshold exchangeability is guaranteed if there is random variation in the continuous assignment variable, e.g., due to random measurement error. Under exchangeability, causal effects can be identified at the threshold. The regression discontinuity intention-to-treat (RD-ITT) effect on an outcome can be estimated as the difference in the outcome between individuals just above (or below) versus just below (or above) the threshold. This effect is analogous to the ITT effect in a randomized controlled trial. Instrumental variable methods can be used to estimate the effect of exposure itself utilizing the threshold as the instrument. We review the recent epidemiologic literature reporting regression discontinuity studies and find that while regression discontinuity designs are beginning to be utilized in a variety of applications in epidemiology, they are still relatively rare, and analytic and reporting practices vary. Regression discontinuity has the potential to greatly contribute to the evidence base in epidemiology, in particular on the real-life and long-term effects and side-effects of medical treatments that are provided based on threshold rules - such as treatments for low birth weight, hypertension or diabetes.

Exercise oscillatory breathing and increased ventilation to carbon dioxide production slope in heart failure: an unfavorable combination with high prognostic value.

PubMed

Guazzi, Marco; Arena, Ross; Ascione, Aniello; Piepoli, Massimo; Guazzi, Maurizio D

2007-05-01

Increased slope of exercise ventilation to carbon dioxide production (VE/VCO2) is an established prognosticator in patients with heart failure. Recently, the occurrence of exercise oscillatory breathing (EOB) has emerged as an additional strong indicator of survival. The aim of this study is to define the respective prognostic significance of these variables and whether excess risk may be identified when either respiratory disorder is present. In 288 stable chronic HF patients (average left ventricular ejection fraction, 33 +/- 13%) who underwent cardiopulmonary exercise testing, the prognostic relevance of VE/VCO2 slope, EOB, and peak VO2 was evaluated by multivariate Cox regression. During a mean interval of 28 +/- 13 months, 62 patients died of cardiac reasons. Thirty-five percent presented with EOB. Among patients exhibiting EOB, 54% had an elevated VE/VCO2 slope. The optimal threshold value for the VE/VCO2 slope identified by receiver operating characteristic analysis was < 36.2 or > or = 36.2 (sensitivity, 77%; specificity, 64%; P < .001). Univariate predictors of death included low left ventricular ejection fraction, low peak VO2, high VE/VCO2 slope, and EOB presence. Multivariate analysis selected EOB as the strongest predictor (chi2, 46.5; P < .001). The VE/VCO2 slope (threshold, < 36.2 or > or = 36.2) was the only other exercise test variable retained in the regression (residual chi2, 5.9; P = .02). The hazard ratio for subjects with EOB and a VE/VCO2 slope > or = 36.2 was 11.4 (95% confidence interval, 4.9-26.5; P < .001). These findings identify EOB as a strong survival predictor even more powerful than VE/VCO2 slope. Exercise oscillatory breathing presence does not necessarily imply an elevated VE/VCO2 slope, but combination of either both yields to a burden of risk remarkably high.

Clinical performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and its derivatives, %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer: results from a multicentre European study, the PROMEtheuS project.

PubMed

Lazzeri, Massimo; Haese, Alexander; Abrate, Alberto; de la Taille, Alexandre; Redorta, Joan Palou; McNicholas, Thomas; Lughezzani, Giovanni; Lista, Giuliana; Larcher, Alessandro; Bini, Vittorio; Cestari, Andrea; Buffi, Nicolòmaria; Graefen, Markus; Bosset, Olivier; Le Corvoisier, Philippe; Breda, Alberto; de la Torre, Pablo; Fowler, Linda; Roux, Jacques; Guazzoni, Giorgio

2013-08-01

To test the sensitivity, specificity and accuracy of serum prostate-specific antigen isoform [-2]proPSA (p2PSA), %p2PSA and the prostate health index (PHI), in men with a family history of prostate cancer (PCa) undergoing prostate biopsy for suspected PCa. To evaluate the potential reduction in unnecessary biopsies and the characteristics of potentially missed cases of PCa that would result from using serum p2PSA, %p2PSA and PHI. The analysis consisted of a nested case-control study from the PRO-PSA Multicentric European Study, the PROMEtheuS project. All patients had a first-degree relative (father, brother, son) with PCa. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 1026 patients included in the PROMEtheuS cohort, 158 (15.4%) had a first-degree relative with PCa. p2PSA, %p2PSA and PHI values were significantly higher (P < 0.001), and free/total PSA (%fPSA) values significantly lower (P < 0.001) in the 71 patients with PCa (44.9%) than in patients without PCa. Univariable accuracy analysis showed %p2PSA (area under the receiver-operating characteristic curve [AUC]: 0.733) and PHI (AUC: 0.733) to be the most accurate predictors of PCa at biopsy, significantly outperforming total PSA ([tPSA] AUC: 0.549), free PSA ([fPSA] AUC: 0.489) and %fPSA (AUC: 0.600) (P ≤ 0.001). For %p2PSA a threshold of 1.66 was found to have the best balance between sensitivity and specificity (70.4 and 70.1%; 95% confidence interval [CI]: 58.4-80.7 and 59.4-79.5 respectively). A PHI threshold of 40 was found to have the best balance between sensitivity and specificity (64.8 and 71.3%, respectively; 95% CI 52.5-75.8 and 60.6-80.5). At 90% sensitivity, the thresholds for %p2PSA and PHI were 1.20 and 25.5, with a specificity of 37.9 and 25.5%, respectively. At a %p2PSA threshold of 1.20, a total of 39 (24.8%) biopsies could have been avoided, but two cancers with a Gleason score (GS) of 7 would have been missed. At a PHI threshold of 25.5 a total of 27 (17.2%) biopsies could have been avoided and two (3.8%) cancers with a GS of 7 would have been missed. In multivariable logistic regression models, %p2PSA and PHI achieved independent predictor status and significantly increased the accuracy of multivariable models including PSA and prostate volume by 8.7 and 10%, respectively (P ≤ 0.001). p2PSA, %p2PSA and PHI were directly correlated with Gleason score (ρ: 0.247, P = 0.038; ρ: 0.366, P = 0.002; ρ: 0.464, P < 0.001, respectively). %p2PSA and PHI are more accurate than tPSA, fPSA and %fPSA in predicting PCa in men with a family history of PCa. Consideration of %p2PSA and PHI results in the avoidance of several unnecessary biopsies. p2PSA, %p2PSA and PHI correlate with cancer aggressiveness. © 2013 BJU International.

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

Physical function interfering with pain and symptoms in fibromyalgia patients.

PubMed

Assumpção, A; Sauer, J F; Mango, P C; Pascual Marques, A

2010-01-01

The aim of this study was to assess the relationship between variables of physical assessment - muscular strength, flexibility and dynamic balance - with pain, pain threshold, and fibromyalgia symptoms (FM). Our sample consists of 55 women, with age ranging from 30 to 55 years (mean of 46.5, (standard deviation, SD=6.6)), mean body mass index (BMI) of 28.7 (3.8) and diagnosed for FM according to the American College of Rheumatology criteria. Pain intensity was measured using a visual analogue scale (VAS) and pain threshold (PT) using Fisher's dolorimeter. FM symptoms were assessed by the Fibromyalgia Impact Questionnaire (FIQ); flexibility by the third finger to floor test (3FF); the muscular strength index (MSI) by the maximum volunteer isometric contraction at flexion and extension of right knee and elbow using a force transducer, dynamic balance by the time to get up and go (TUG) test and the functional reach test (FRT). Data were analysed using Pearson's correlation, as well as simple and multivariate regression tests, with significance level of 5%. PT and FIQ were weakly but significantly correlated with the TUG, MSI and 3FF as well as VAS with the TUG and MSI (p<0.05). VAS, PT and FIQ was not correlated with FRT. Simple regression suggests that, alone, TUG, FR, MSI and 3FF are low predictors of VAS, PT and FIQ. For the VAS, the best predictive model includes TUG and MSI, explaining 12.6% of pain variability. For TP and total symptoms, as obtained by the FIQ, most predictive model includes 3FF and MSI, which respectively respond by 30% and 21% of the variability. Muscular strength, flexibility and balance are associated with pain, pain threshold, and symptoms in FM patients.

Quality of life in childhood, adolescence and adult food allergy: Patient and parent perspectives.

PubMed

Stensgaard, A; Bindslev-Jensen, C; Nielsen, D; Munch, M; DunnGalvin, A

2017-04-01

Studies of children with food allergy typically only include the mother and have not investigated the relationship between the amount of allergen needed to elicit a clinical reaction (threshold) and health-related quality of life (HRQL). Our aims were (i) to compare self-reported and parent-reported HRQL in different age groups, (ii) to evaluate the impact of severity of allergic reaction and threshold on HRQL, and (iii) to investigate factors associated with patient-reported and parent-reported HRQL. Age-appropriate Food Allergy Quality of Life Questionnaires (FAQLQ) were completed by 73 children, 49 adolescents and 29 adults with peanut, hazelnut or egg allergy. Parents (197 mothers, 120 fathers) assessed their child's HRQL using the FAQLQ-Parent form. Clinical data and threshold values were obtained from a hospital database. Significant factors for HRQL were investigated using univariate and multivariate regression. Female patients reported greater impact of food allergy on HRQL than males did. Egg and hazelnut thresholds did not affect HRQL, but lower peanut threshold was associated with worse HRQL. Both parents scored their child's HRQL better than the child's own assessment, but whereas mother-reported HRQL was significantly affected by limitations in the child's social life, father-reported HRQL was affected by limitations in the family's social life. Severity of allergic reaction did not contribute significantly to HRQL. The risk of accidental allergen ingestion and limitations in social life are associated with worse HRQL. Fathers provide a unique perspective and should have a greater opportunity to contribute to food allergy research. © 2016 John Wiley & Sons Ltd.

Development of an anaerobic threshold (HRLT, HRVT) estimation equation using the heart rate threshold (HRT) during the treadmill incremental exercise test

PubMed Central

Ham, Joo-ho; Park, Hun-Young; Kim, Youn-ho; Bae, Sang-kon; Ko, Byung-hoon

2017-01-01

[Purpose] The purpose of this study was to develop a regression model to estimate the heart rate at the lactate threshold (HRLT) and the heart rate at the ventilatory threshold (HRVT) using the heart rate threshold (HRT), and to test the validity of the regression model. [Methods] We performed a graded exercise test with a treadmill in 220 normal individuals (men: 112, women: 108) aged 20–59 years. HRT, HRLT, and HRVT were measured in all subjects. A regression model was developed to estimate HRLT and HRVT using HRT with 70% of the data (men: 79, women: 76) through randomization (7:3), with the Bernoulli trial. The validity of the regression model developed with the remaining 30% of the data (men: 33, women: 32) was also examined. [Results] Based on the regression coefficient, we found that the independent variable HRT was a significant variable in all regression models. The adjusted R2 of the developed regression models averaged about 70%, and the standard error of estimation of the validity test results was 11 bpm, which is similar to that of the developed model. [Conclusion] These results suggest that HRT is a useful parameter for predicting HRLT and HRVT. PMID:29036765

Development of an anaerobic threshold (HRLT, HRVT) estimation equation using the heart rate threshold (HRT) during the treadmill incremental exercise test.

PubMed

Ham, Joo-Ho; Park, Hun-Young; Kim, Youn-Ho; Bae, Sang-Kon; Ko, Byung-Hoon; Nam, Sang-Seok

2017-09-30

The purpose of this study was to develop a regression model to estimate the heart rate at the lactate threshold (HRLT) and the heart rate at the ventilatory threshold (HRVT) using the heart rate threshold (HRT), and to test the validity of the regression model. We performed a graded exercise test with a treadmill in 220 normal individuals (men: 112, women: 108) aged 20-59 years. HRT, HRLT, and HRVT were measured in all subjects. A regression model was developed to estimate HRLT and HRVT using HRT with 70% of the data (men: 79, women: 76) through randomization (7:3), with the Bernoulli trial. The validity of the regression model developed with the remaining 30% of the data (men: 33, women: 32) was also examined. Based on the regression coefficient, we found that the independent variable HRT was a significant variable in all regression models. The adjusted R2 of the developed regression models averaged about 70%, and the standard error of estimation of the validity test results was 11 bpm, which is similar to that of the developed model. These results suggest that HRT is a useful parameter for predicting HRLT and HRVT. ©2017 The Korean Society for Exercise Nutrition

The consequences of ignoring measurement invariance for path coefficients in structural equation models

PubMed Central

Guenole, Nigel; Brown, Anna

2014-01-01

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. PMID:25278911

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

Multivariate Analyses of Balance Test Performance, Vestibular Thresholds, and Age

PubMed Central

Karmali, Faisal; Bermúdez Rey, María Carolina; Clark, Torin K.; Wang, Wei; Merfeld, Daniel M.

2017-01-01

We previously published vestibular perceptual thresholds and performance in the Modified Romberg Test of Standing Balance in 105 healthy humans ranging from ages 18 to 80 (1). Self-motion thresholds in the dark included roll tilt about an earth-horizontal axis at 0.2 and 1 Hz, yaw rotation about an earth-vertical axis at 1 Hz, y-translation (interaural/lateral) at 1 Hz, and z-translation (vertical) at 1 Hz. In this study, we focus on multiple variable analyses not reported in the earlier study. Specifically, we investigate correlations (1) among the five thresholds measured and (2) between thresholds, age, and the chance of failing condition 4 of the balance test, which increases vestibular reliance by having subjects stand on foam with eyes closed. We found moderate correlations (0.30–0.51) between vestibular thresholds for different motions, both before and after using our published aging regression to remove age effects. We found that lower or higher thresholds across all threshold measures are an individual trait that account for about 60% of the variation in the population. This can be further distributed into two components with about 20% of the variation explained by aging and 40% of variation explained by a single principal component that includes similar contributions from all threshold measures. When only roll tilt 0.2 Hz thresholds and age were analyzed together, we found that the chance of failing condition 4 depends significantly on both (p = 0.006 and p = 0.013, respectively). An analysis incorporating more variables found that the chance of failing condition 4 depended significantly only on roll tilt 0.2 Hz thresholds (p = 0.046) and not age (p = 0.10), sex nor any of the other four threshold measures, suggesting that some of the age effect might be captured by the fact that vestibular thresholds increase with age. For example, at 60 years of age, the chance of failing is roughly 5% for the lowest roll tilt thresholds in our population, but this increases to 80% for the highest roll tilt thresholds. These findings demonstrate the importance of roll tilt vestibular cues for balance, even in individuals reporting no vestibular symptoms and with no evidence of vestibular dysfunction. PMID:29167656

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Empirical relationships among oliguria, creatinine, mortality, and renal replacement therapy in the critically ill.

PubMed

Mandelbaum, Tal; Lee, Joon; Scott, Daniel J; Mark, Roger G; Malhotra, Atul; Howell, Michael D; Talmor, Daniel

2013-03-01

The observation periods and thresholds of serum creatinine and urine output defined in the Acute Kidney Injury Network (AKIN) criteria were not empirically derived. By continuously varying creatinine/urine output thresholds as well as the observation period, we sought to investigate the empirical relationships among creatinine, oliguria, in-hospital mortality, and receipt of renal replacement therapy (RRT). Using a high-resolution database (Multiparameter Intelligent Monitoring in Intensive Care II), we extracted data from 17,227 critically ill patients with an in-hospital mortality rate of 10.9 %. The 14,526 patients had urine output measurements. Various combinations of creatinine/urine output thresholds and observation periods were investigated by building multivariate logistic regression models for in-hospital mortality and RRT predictions. For creatinine, both absolute and percentage increases were analyzed. To visualize the dependence of adjusted mortality and RRT rate on creatinine, the urine output, and the observation period, we generated contour plots. Mortality risk was high when absolute creatinine increase was high regardless of the observation period, when percentage creatinine increase was high and the observation period was long, and when oliguria was sustained for a long period of time. Similar contour patterns emerged for RRT. The variability in predictive accuracy was small across different combinations of thresholds and observation periods. The contour plots presented in this article complement the AKIN definition. A multi-center study should confirm the universal validity of the results presented in this article.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

USDA-ARS?s Scientific Manuscript database

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...

Generation of multivariate near shore extreme wave conditions based on an extreme value copula for offshore boundary conditions.

NASA Astrophysics Data System (ADS)

Leyssen, Gert; Mercelis, Peter; De Schoesitter, Philippe; Blanckaert, Joris

2013-04-01

Near shore extreme wave conditions, used as input for numerical wave agitation simulations and for the dimensioning of coastal defense structures, need to be determined at a harbour entrance situated at the French North Sea coast. To obtain significant wave heights, the numerical wave model SWAN has been used. A multivariate approach was used to account for the joint probabilities. Considered variables are: wind velocity and direction, water level and significant offshore wave height and wave period. In a first step a univariate extreme value distribution has been determined for the main variables. By means of a technique based on the mean excess function, an appropriate member of the GPD is selected. An optimal threshold for peak over threshold selection is determined by maximum likelihood optimization. Next, the joint dependency structure for the primary random variables is modeled by an extreme value copula. Eventually the multivariate domain of variables was stratified in different classes, each of which representing a combination of variable quantiles with a joint probability, which are used for model simulation. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The analysis is repeated for 9 different wind directions. The secondary variable is water level. In shallow waters extreme waves will be directly affected by water depth. Hence the joint probability of occurrence for water level and wave height is of major importance for design of coastal defense structures. Wind velocity and water levels are only dependent for some wind directions (wind induced setup). Dependent directions are detected using a Kendall and Spearman test and appeared to be those with the longest fetch. For these directions, wind velocity and water level extreme value distributions are multivariately linked through a Gumbel Copula. These distributions are stratified into classes of which the frequency of occurrence can be calculated. For the remaining directions the univariate extreme wind velocity distribution is stratified, each class combined with 5 high water levels. The wave height at the model boundaries was taken into account by a regression with the extreme wind velocity at the offshore location. The regression line and the 95% confidence limits where combined with each class. Eventually the wave period is computed by a new regression with the significant wave height. This way 1103 synthetic events were selected and simulated with the SWAN wave model, each of which a frequency of occurrence is calculated for. Hence near shore significant wave heights are obtained with corresponding frequencies. The statistical distribution of the near shore wave heights is determined by sorting the model results in a descending order and accumulating the corresponding frequencies. This approach allows determination of conditional return periods. For example, for the imposed univariate design return periods of 100 years for significant wave height and 30 years for water level, the joint return period for a simultaneous exceedance of both conditions can be computed as 4000 years. Hence, this methodology allows for a probabilistic design of coastal defense structures.

Regression Discontinuity Designs in Epidemiology

PubMed Central

Moscoe, Ellen; Mutevedzi, Portia; Newell, Marie-Louise; Bärnighausen, Till

2014-01-01

When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology. PMID:25061922

All-cause mortality in asymptomatic persons with extensive Agatston scores above 1000.

PubMed

Patel, Jaideep; Blaha, Michael J; McEvoy, John W; Qadir, Sadia; Tota-Maharaj, Rajesh; Shaw, Leslee J; Rumberger, John A; Callister, Tracy Q; Berman, Daniel S; Min, James K; Raggi, Paolo; Agatston, Arthur A; Blumenthal, Roger S; Budoff, Matthew J; Nasir, Khurram

2014-01-01

Risk assessment in the extensive calcified plaque phenotype has been limited by small sample size. We studied all-cause mortality rates among asymptomatic patients with markedly elevated Agatston scores > 1000. We studied a clinical cohort of 44,052 asymptomatic patients referred for coronary calcium scans. Mean follow-up was 5.6 years (range, 1-13 years). All-cause mortality rates were calculated after stratifying by Agatston score (0, 1-1000, 1001-1500, 1500-2000, and >2000). A multivariable Cox regression model adjusting for self-reported traditional risk factors was created to assess the relative mortality hazard of Agatston scores 1001 to 1500, 1501 to 2000, and >2000. With the use of post-estimation modeling, we assessed for the presence of an upper threshold of risk with high Agatston scores. A total of 1593 patients (4% of total population) had Agatston score > 1000. There was a continuous graded decrease in estimated 10-year survival across increasing Agatston score, continuing when Agatston score > 1000 (Agatston score 1001-1500, 78%; Agatston score 1501-2000, 74%; Agatston score > 2000, 51%). After multivariable adjustment, Agatston scores 1001 to 1500, 1501 to 2000, and >2000 were associated with an 8.05-, 7.45-, and 13.26-fold greater mortality risk, respectively, than for Agatston score of 0. Compared with Agatston score 1001 to 1500, Agatston score 1501 to 2000 had a similar all-cause mortality risk, whereas Agatston score > 2000 had an increased relative risk (Agatston score 1501-2000: hazard ratio [HR], 1.01 [95% CI, 0.67-1.51]; Agatston score > 2000: HR, 1.79 [95% CI, 1.30-2.46]). Graphical assessment of the predicted survival model suggests no upper threshold for risk associated with calcified plaque in coronary arteries. Increasing calcified plaque in coronary arteries continues to predict a graded decrease in survival among patients with extensive Agatston score > 1000 with no apparent upper threshold. Published by Elsevier Inc.

Pressure pain thresholds and musculoskeletal morbidity in automobile manufacturing workers.

PubMed

Gold, Judith E; Punnett, Laura; Katz, Jeffrey N

2006-02-01

Reduced pressure pain thresholds (PPTs) have been reported in occupational groups with symptoms of upper extremity musculoskeletal disorders (UEMSDs). The purpose of this study was to determine whether automobile manufacturing workers (n=460) with signs and symptoms of UEMSDs had reduced PPTs (greater sensitivity to pain through pressure applied to the skin) when compared with unaffected members of the cohort, which served as the reference group. The association of PPTs with symptom severity and localization of PE findings was investigated, as was the hypothesis that reduced thresholds would be found on the affected side in those with unilateral physical examination (PE) findings. PPTs were measured during the workday at 12 upper extremity sites. A PE for signs of UEMSDs and symptom questionnaire was administered. After comparison of potential covariates using t tests, linear regression multivariable models were constructed with the average of 12 sites (avgPPT) as the outcome. Subjects with PE findings and/or symptoms had a statistically significant lower avgPPT than non-cases. AvgPPT was reduced in those with more widespread PE findings and in those with greater symptom severity (test for trend, P

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

PubMed

Bütof, Rebecca; Hofheinz, Frank; Zöphel, Klaus; Stadelmann, Tobias; Schmollack, Julia; Jentsch, Christina; Löck, Steffen; Kotzerke, Jörg; Baumann, Michael; van den Hoff, Jörg

2015-08-01

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

HbA1c is significantly associated with arterial stiffness but not with carotid atherosclerosis in a community-based population without type 2 diabetes: The Dong-gu study.

PubMed

Lee, Young-Hoon; Shin, Min-Ho; Choi, Jin-Su; Rhee, Jung-Ae; Nam, Hae-Sung; Jeong, Seul-Ki; Park, Kyeong-Soo; Ryu, So-Yeon; Choi, Seong-Woo; Kim, Bok-Hee; Oh, Gyung-Jae; Kweon, Sun-Seog

2016-04-01

We examined the associations between HbA1c levels and various atherosclerotic vascular parameters among adults without diabetes from the general population. A total of 6500 community-dwelling adults, who were free of type 2 diabetes and ≥50 years of age, were included. High-resolution B-mode ultrasound was used to evaluate carotid artery structure, including intima-media thickness (IMT), plaque, and luminal diameter. Brachial-ankle pulse wave velocity (baPWV), which is a useful indicator of systemic arterial stiffness, was determined using an automatic waveform analysis device. No significant associations were observed between HbA1c, carotid IMT, plaque, or luminal diameter in a fully adjusted model. However, the odds ratio (95% confidence interval) for high baPWV (defined as the highest quartile) increased by 1.43 (1.19-1.71) per 1% HbA1c increase after adjusting for conventional risk factors in a multivariate logistic regression analysis. In addition, HbA1c was independently associated with baPWV in a multivariate linear regression analysis. High-normal HbA1c level was independently associated with arterial stiffness, but not with carotid atherosclerotic parameters, in the general population without diabetes. Our results suggest that the functional atherosclerotic process may already be accelerated according to HbA1c level, even at a level below the diagnostic threshold for diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Stroke Severity Affects Timing: Time From Stroke Code Activation to Initial Imaging is Longer in Patients With Milder Strokes.

PubMed

Kwei, Kimberly T; Liang, John; Wilson, Natalie; Tuhrim, Stanley; Dhamoon, Mandip

2018-05-01

Optimizing the time it takes to get a potential stroke patient to imaging is essential in a rapid stroke response. At our hospital, door-to-imaging time is comprised of 2 time periods: the time before a stroke is recognized, followed by the period after the stroke code is called during which the stroke team assesses and brings the patient to the computed tomography scanner. To control for delays due to triage, we isolated the time period after a potential stroke has been recognized, as few studies have examined the biases of stroke code responders. This "code-to-imaging time" (CIT) encompassed the time from stroke code activation to initial imaging, and we hypothesized that perception of stroke severity would affect how quickly stroke code responders act. In consecutively admitted ischemic stroke patients at The Mount Sinai Hospital emergency department, we tested associations between National Institutes of Health Stroke Scale scores (NIHSS), continuously and at different cutoffs, and CIT using spline regression, t tests for univariate analysis, and multivariable linear regression adjusting for age, sex, and race/ethnicity. In our study population, mean CIT was 26 minutes, and mean presentation NIHSS was 8. In univariate and multivariate analyses comparing CIT between mild and severe strokes, stroke scale scores <4 were associated with longer response times. Milder strokes are associated with a longer CIT with a threshold effect at a NIHSS of 4.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy.

PubMed

Mooij, Anne H; Frauscher, Birgit; Amiri, Mina; Otte, Willem M; Gotman, Jean

2016-12-01

To assess whether there is a difference in the background activity in the ripple band (80-200Hz) between epileptic and non-epileptic channels, and to assess whether this difference is sufficient for their reliable separation. We calculated mean and standard deviation of wavelet entropy in 303 non-epileptic and 334 epileptic channels from 50 patients with intracerebral depth electrodes and used these measures as predictors in a multivariable logistic regression model. We assessed sensitivity, positive predictive value (PPV) and negative predictive value (NPV) based on a probability threshold corresponding to 90% specificity. The probability of a channel being epileptic increased with higher mean (p=0.004) and particularly with higher standard deviation (p<0.0001). The performance of the model was however not sufficient for fully classifying the channels. With a threshold corresponding to 90% specificity, sensitivity was 37%, PPV was 80%, and NPV was 56%. A channel with a high standard deviation of entropy is likely to be epileptic; with a threshold corresponding to 90% specificity our model can reliably select a subset of epileptic channels. Most studies have concentrated on brief ripple events. We showed that background activity in the ripple band also has some ability to discriminate epileptic channels. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Stapedotomy in osteogenesis imperfecta: a prospective study of 32 consecutive cases.

PubMed

Vincent, Robert; Wegner, Inge; Stegeman, Inge; Grolman, Wilko

2014-12-01

To prospectively evaluate hearing outcomes in patients with osteogenesis imperfecta undergoing primary stapes surgery and to isolate prognostic factors for success. A nonrandomized, open, prospective case series. A tertiary referral center. Twenty-five consecutive patients who underwent 32 primary stapedotomies for osteogenesis imperfecta with evidence of stapes fixation and available postoperative pure-tone audiometry. Primary stapedotomy with vein graft interposition and reconstruction with a regular Teflon piston or bucket handle-type piston. Preoperative and postoperative audiometric evaluation using conventional 4-frequency (0.5, 1, 2, and 4 kHz) audiometry. Air-conduction thresholds, bone-conduction thresholds, and air-bone gap were measured. The overall audiometric results as well as the results of audiometric evaluation at 3 months and at least 1 year after surgery were used. Overall, postoperative air-bone gap closure to within 10 dB was achieved in 88% of cases. Mean (standard deviation) gain in air-conduction threshold was 22 (9.4) dB for the entire case series, and mean (standard deviation) air-bone gap closure was 22 (9.0) dB. Backward multivariate logistic regression showed that a model with preoperative air-bone gap closure and intraoperatively established incus length accurately predicts success after primary stapes surgery. Stapes surgery is a feasible and safe treatment option in patients with osteogenesis imperfecta. Success is associated with preoperative air-bone gap and intraoperatively established incus length.

Correlation between PET/CT parameters and KRAS expression in colorectal cancer.

PubMed

Chen, Shang-Wen; Chiang, Hua-Che; Chen, William Tzu-Liang; Hsieh, Te-Chun; Yen, Kuo-Yang; Chiang, Shu-Fen; Kao, Chia-Hung

2014-08-01

The objective of this study was to correlate the association between mutated KRAS and wild-type colorectal cancer (CRC) by using various F-FDG PET-related parameters. One hundred twenty-one CRC patients who had undergone preoperative PET/CT were included in this study. Several PET/CT-related parameters, including SUVmax and various thresholds of metabolic tumor volume, total lesion glycolysis, and PET/CT-based tumor width, were measured. Tumor- and PET/CT-related parameters were correlated with genomic expression between KRAS mutant and wild-type groups, using a Mann-Whitney U test and logistic regression analysis. Colorectal cancer tumors with a mutated KRAS exhibited higher SUVmax and an increased accumulation of FDG among several threshold methods. Multivariate analysis showed that SUVmax and using a 40% threshold level for maximal uptake of TW (TW40%) were the 2 predictors of KRAS mutations. The odds ratio was 1.23 for SUVmax (P = 0.02; 95% confidence interval, 1.01-1.52) and 1.15 for TW40% (P = 0.02; 95% confidence interval, 1.02-1.30). The accuracy of SUVmax for predicting mutated KRAS was higher in patients with colon or sigmoid colon cancers, whereas it was TW40% in those with rectal cancers. SUVmax and TW40% were associated in CRC with KRAS mutations. PET/CT parameters can supplement genomic analysis to determine KRAS expression in CRC.

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Association of aortic wall thickness on contrast-enhanced chest CT with major cerebro-cardiac events.

PubMed

Tresoldi, Silvia; Di Leo, Giovanni; Zoffoli, Elena; Munari, Alice; Primolevo, Alessandra; Cornalba, Gianpaolo; Sardanelli, Francesco

2014-11-01

There is a significant association between aortic atherosclerosis and previous major cardiovascular events. Particularly, thoracic aortic atherosclerosis is closely related to the degree of coronary and carotid artery disease. Thus, there is a rationale for screening the thoracic aorta in patients who undergo a chest computed tomography (CT) for any clinical question, in order to detect patients at increased risk of cerebro-cardiovascular (CCV) events. To estimate the association between either thoracic aortic wall thickness (AWT) or aortic total calcium score (ATCS) and CCV events. One hundred and forty-eight non-cardiac patients (78 men; 67 ± 12 years) underwent chest contrast-enhanced multidetector CT (MDCT). The AWT was measured at the level of the left atrium (AWTref) and at the maximum AWT (AWTmax). Correlation with clinical CCV patients' history was estimated. The value of AWTmax and of a semi-quantitative ATCS as a marker for CCV events was assessed using receiver-operating characteristic curve (ROC) analysis and multivariate regression analysis. Out of 148 patients, 59% reported sedentary lifestyle, 44% hypertension, 32% smoking, 23% hypercholesterolemia, 13% family history of cardiac disease, 12% diabetes, and 10% BMI ≥ 30 kg/m(2); 9% reported myocardial infarction, 8% aortic aneurism, 8% myocardial revascularization, and 2% ischemic stroke. Twenty-six percent of patients had a medium-to-high ATCS. Both AWTmax and AWTref correlated with hypertension and age (P < 0.002). At the ROC analysis, a 4.8 mm threshold was associated to a 90% specificity and an odds ratio of 6.3 (AUC = 0.735). Assuming as threshold the AWTmax median value (4.3 mm) of patients who suffered from at least one CCV event in their history, a negative predictive value of 90%, a RR of 3.6 and an OR of 6.3 were found. At the multivariate regression analysis, AWTmax was the only independent variable associated to the frequency of CCV events. Patients with increased thoracic AWTmax on chest MDCT could be considered at risk for CCV disease. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Prevalence of subclinical ketosis and relationships with postpartum diseases in European dairy cows.

PubMed

Suthar, V S; Canelas-Raposo, J; Deniz, A; Heuwieser, W

2013-05-01

Subclinical ketosis (SCK) is defined as concentrations of β-hydroxybutyrate (BHBA) ≥ 1.2 to 1.4 mmol/L and it is considered a gateway condition for other metabolic and infectious disorders such as metritis, mastitis, clinical ketosis, and displaced abomasum. Reported prevalence rates range from 6.9 to 43% in the first 2 mo of lactation. However, there is a dearth of information on prevalence rates considering the diversity of European dairy farms. The objectives of this study were to (1) determine prevalence of SCK, (2) identify thresholds of BHBA, and (3) study their relationships with postpartum metritis, clinical ketosis, displaced abomasum, lameness, and mastitis in European dairy farms. From May to October 2011, a convenience sample of 528 dairy herds from Croatia, Germany, Hungary, Italy, Poland, Portugal, Serbia, Slovenia, Spain, and Turkey was studied. β-Hydroxybutyrate levels were measured in 5,884 cows with a handheld meter within 2 to 15 d in milk (DIM). On average, 11 cows were enrolled per farm and relevant information (e.g., DIM, postpartum diseases, herd size) was recorded. Using receiver operator characteristic curve analyses, blood BHBA thresholds were determined for the occurrence of metritis, mastitis, clinical ketosis, displaced abomasum, and lameness. Multivariate binary logistic regression models were built for each disease, considering cow as the experimental unit and herd as a random effect. Overall prevalence of SCK (i.e., blood BHBA ≥ 1.2 mmol/L) within 10 countries was 21.8%, ranging from 11.2 to 36.6%. Cows with SCK had 1.5, 9.5, and 5.0 times greater odds of developing metritis, clinical ketosis, and displaced abomasum, respectively. Multivariate binary logistic regression models demonstrated that cows with blood BHBA levels of ≥ 1.4, ≥ 1.1 and ≥ 1.7 mmol/L during 2 to 15 DIM had 1.7, 10.5, and 6.9 times greater odds of developing metritis, clinical ketosis, and displaced abomasum, respectively, compared with cows with lower BHBA blood levels. Interestingly, a postpartum blood BHBA threshold ≥ 1.1 mmol/L increased the odds for lameness in dairy cows 1.8 (95% confidence interval: 1.3 to 2.5) times. Overall, prevalence of SCK was high between 2 to 15 DIM and SCK increased the odds of metritis, clinical ketosis, lameness, and displaced abomasum in European dairy herds. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Clinical evaluation of an inspiratory impedance threshold device during standard cardiopulmonary resuscitation in patients with out-of-hospital cardiac arrest.

PubMed

Aufderheide, Tom P; Pirrallo, Ronald G; Provo, Terry A; Lurie, Keith G

2005-04-01

To determine whether an impedance threshold device, designed to enhance circulation, would increase acute resuscitation rates for patients in cardiac arrest receiving conventional manual cardiopulmonary resuscitation. Prospective, randomized, double-blind, intention-to-treat. Out-of-hospital trial conducted in the Milwaukee, WI, emergency medical services system. Adults in cardiac arrest of presumed cardiac etiology. On arrival of advanced life support, patients were treated with standard cardiopulmonary resuscitation combined with either an active or a sham impedance threshold device. We measured safety and efficacy of the impedance threshold device; the primary end point was intensive care unit admission. Statistical analyses performed included the chi-square test and multivariate regression analysis. One hundred sixteen patients were treated with a sham impedance threshold device, and 114 patients were treated with an active impedance threshold device. Overall intensive care unit admission rates were 17% with the sham device vs. 25% in the active impedance threshold device (p = .13; odds ratio, 1.64; 95% confidence interval, 0.87, 3.10). Patients in the subgroup presenting with pulseless electrical activity had intensive care unit admission and 24-hr survival rates of 20% and 12% in sham (n = 25) vs. 52% and 30% in active impedance threshold device groups (n = 27) (p = .018, odds ratio, 4.31; 95% confidence interval, 1.28, 14.5, and p = .12, odds ratio, 3.09; 95% confidence interval, 0.74, 13.0, respectively). A post hoc analysis of patients with pulseless electrical activity at any time during the cardiac arrest revealed that intensive care unit and 24-hr survival rates were 20% and 11% in the sham (n = 56) vs. 41% and 27% in the active impedance threshold device groups (n = 49) (p = .018, odds ratio, 2.82; 95% confidence interval, 1.19, 6.67, and p = .037, odds ratio, 3.01; 95% confidence interval, 1.07, 8.96, respectively). There were no statistically significant differences in outcomes for patients presenting in ventricular fibrillation and asystole. Adverse event and complication rates were also similar. During this first clinical trial of the impedance threshold device during standard cardiopulmonary resuscitation, use of the new device more than doubled short-term survival rates in patients presenting with pulseless electrical activity. A larger clinical trial is underway to determine the potential longer term benefits of the impedance threshold device in cardiac arrest.

Prediction of insufficient serum vitamin D status in older women: a validated model.

PubMed

Merlijn, T; Swart, K M A; Lips, P; Heymans, M W; Sohl, E; Van Schoor, N M; Netelenbos, C J; Elders, P J M

2018-05-28

We developed an externally validated simple prediction model to predict serum 25(OH)D levels < 30, < 40, < 50 and 60 nmol/L in older women with risk factors for fractures. The benefit of the model reduces when a higher 25(OH)D threshold is chosen. Vitamin D deficiency is associated with increased fracture risk in older persons. General supplementation of all older women with vitamin D could cause medicalization and costs. We developed a clinical model to identify insufficient serum 25-hydroxyvitamin D (25(OH)D) status in older women at risk for fractures. In a sample of 2689 women ≥ 65 years selected from general practices, with at least one risk factor for fractures, a questionnaire was administered and serum 25(OH)D was measured. Multivariable logistic regression models with backward selection were developed to select predictors for insufficient serum 25(OH)D status, using separate thresholds 30, 40, 50 and 60 nmol/L. Internal and external model validations were performed. Predictors in the models were as follows: age, BMI, vitamin D supplementation, multivitamin supplementation, calcium supplementation, daily use of margarine, fatty fish ≥ 2×/week, ≥ 1 hours/day outdoors in summer, season of blood sampling, the use of a walking aid and smoking. The AUC was 0.77 for the model using a 30 nmol/L threshold and decreased in the models with higher thresholds to 0.72 for 60 nmol/L. We demonstrate that the model can help to distinguish patients with or without insufficient serum 25(OH)D levels at thresholds of 30 and 40 nmol/L, but not when a threshold of 50 nmol/L is demanded. This externally validated model can predict the presence of vitamin D insufficiency in women at risk for fractures. The potential clinical benefit of this tool is highly dependent of the chosen 25(OH)D threshold and decreases when a higher threshold is used.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

A Continuous Threshold Expectile Model.

PubMed

Zhang, Feipeng; Li, Qunhua

2017-12-01

Expectile regression is a useful tool for exploring the relation between the response and the explanatory variables beyond the conditional mean. A continuous threshold expectile regression is developed for modeling data in which the effect of a covariate on the response variable is linear but varies below and above an unknown threshold in a continuous way. The estimators for the threshold and the regression coefficients are obtained using a grid search approach. The asymptotic properties for all the estimators are derived, and the estimator for the threshold is shown to achieve root-n consistency. A weighted CUSUM type test statistic is proposed for the existence of a threshold at a given expectile, and its asymptotic properties are derived under both the null and the local alternative models. This test only requires fitting the model under the null hypothesis in the absence of a threshold, thus it is computationally more efficient than the likelihood-ratio type tests. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic cases. The application of the proposed method on a Dutch growth data and a baseball pitcher salary data reveals interesting insights. The proposed method is implemented in the R package cthreshER .

Do Hearing Protectors Protect Hearing?

PubMed Central

Groenewold, Matthew R.; Masterson, Elizabeth A.; Themann, Christa L.; Davis, Rickie R.

2015-01-01

Background We examined the association between self-reported hearing protection use at work and incidence of hearing shifts over a 5-year period. Methods Audiometric data from 19,911 workers were analyzed. Two hearing shift measures—OSHA standard threshold shift (OSTS) and high-frequency threshold shift (HFTS)—were used to identify incident shifts in hearing between workers’ 2005 and 2009 audiograms. Adjusted odds ratios were generated using multivariable logistic regression with multi-level modeling. Results The odds ratio for hearing shift for workers who reported never versus always wearing hearing protection was nonsignificant for OSTS (OR 1.23, 95% CI 0.92–1.64) and marginally significant for HFTS (OR 1.26, 95% CI 1.00–1.59). A significant linear trend towards increased risk of HFTS with decreased use of hearing protection was observed (P = 0.02). Conclusion The study raises concern about the effectiveness of hearing protection as a substitute for noise control to prevent noise-induced hearing loss in the workplace. Am. J. Ind. Med. 57:1001–1010, 2014. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. PMID:24700499

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Alternatives for using multivariate regression to adjust prospective payment rates

PubMed Central

Sheingold, Steven H.

1990-01-01

Multivariate regression analysis has been used in structuring three of the adjustments to Medicare's prospective payment rates. Because the indirect-teaching adjustment, the disproportionate-share adjustment, and the adjustment for large cities are responsible for distributing approximately $3 billion in payments each year, the specification of regression models for these adjustments is of critical importance. In this article, the application of regression for adjusting Medicare's prospective rates is discussed, and the implications that differing specifications could have for these adjustments are demonstrated. PMID:10113271

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Influence of intravenous opioid dose on postoperative ileus.

PubMed

Barletta, Jeffrey F; Asgeirsson, Theodor; Senagore, Anthony J

2011-07-01

Intravenous opioids represent a major component in the pathophysiology of postoperative ileus (POI). However, the most appropriate measure and threshold to quantify the association between opioid dose (eg, average daily, cumulative, maximum daily) and POI remains unknown. To evaluate the relationship between opioid dose, POI, and length of stay (LOS) and identify the opioid measure that was most strongly associated with POI. Consecutive patients admitted to a community teaching hospital who underwent elective colorectal surgery by any technique with an enhanced-recovery protocol postoperatively were retrospectively identified. Patients were excluded if they received epidural analgesia, developed a major intraabdominal complication or medical complication, or had a prolonged workup prior to surgery. Intravenous opioid doses were quantified and converted to hydromorphone equivalents. Classification and regression tree (CART) analysis was used to determine the dosing threshold for the opioid measure most associated with POI and define high versus low use of opioids. Risk factors for POI and prolonged LOS were determined through multivariate analysis. The incidence of POI in 279 patients was 8.6%. CART analysis identified a maximum daily intravenous hydromorphone dose of 2 mg or more as the opioid measure most associated with POI. Multivariate analysis revealed maximum daily hydromorphone dose of 2 mg or more (p = 0.034), open surgical technique (p = 0.045), and days of intravenous narcotic therapy (p = 0.003) as significant risk factors for POI. Variables associated with increased LOS were POI (p < 0.001), maximum daily hydromorphone dose of 2 mg or more (p < 0.001), and age (p = 0.005); laparoscopy (p < 0.001) was associated with a decreased LOS. Intravenous opioid therapy is significantly associated with POI and prolonged LOS, particularly when the maximum hydromorphone dose per day exceeds 2 mg. Clinicians should consider alternative, nonopioid-based pain management options when this occurs.

Satisfaction and adherence in patients with iron overload receiving iron chelation therapy as assessed by a newly developed patient instrument.

PubMed

Rofail, Diana; Abetz, Linda; Viala, Muriel; Gait, Claire; Baladi, Jean-Francois; Payne, Krista

2009-01-01

This study assesses satisfaction with iron chelation therapy (ICT) based on a reliable and valid instrument, and explores the relationship between satisfaction and adherence to ICT. Patients in the USA and UK completed a new "Satisfaction with ICT" (SICT) instrument consisting of 28 items, three pertaining to adherence. Simple and multivariate regression analyses assessed the relationship between satisfaction with different aspects of ICT and adherence. First assessments of the SICT instrument indicate its validity and reliability. Recommended thresholds for internal consistency, convergent validity, discriminant validity, and floor and ceiling effects were met. A number of variables were identified in the simple linear regression analyses as significant predictors of "never thinking about stopping ICT," a proxy for adherence. These significant variables were entered into the multivariate model to assess the combined factor effects, explaining 42% of the total variance of "never thinking about stopping ICT." A significant and positive relationship was demonstrated between "never thinking about stopping ICT" and age (P = 0.04), Perceived Effectiveness of ICT (P = 0.003), low Burden of ICT (P = 0.002), and low Side Effects of ICT (P = 0.01). The SICT is a reliable and valid instrument which will be useful in ICT clinical trials. Furthermore, the administration of ICT by slow subcutaneous infusion negatively impacts on satisfaction with ICT which was shown to be a determinant of adherence. This points to the need for new more convenient and less burdensome oral iron chelators to increase adherence, and ultimately to improve patient outcomes.

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Threshold altitude resulting in decompression sickness

NASA Technical Reports Server (NTRS)

Kumar, K. V.; Waligora, James M.; Calkins, Dick S.

1990-01-01

A review of case reports, hypobaric chamber training data, and experimental evidence indicated that the threshold for incidence of altitude decompression sickness (DCS) was influenced by various factors such as prior denitrogenation, exercise or rest, and period of exposure, in addition to individual susceptibility. Fitting these data with appropriate statistical models makes it possible to examine the influence of various factors on the threshold for DCS. This approach was illustrated by logistic regression analysis on the incidence of DCS below 9144 m. Estimations using these regressions showed that, under a noprebreathe, 6-h exposure, simulated EVA profile, the threshold for symptoms occurred at approximately 3353 m; while under a noprebreathe, 2-h exposure profile with knee-bends exercise, the threshold occurred at 7925 m.

Assessing the economic burden of illness for tuberculosis patients in Benin: determinants and consequences of catastrophic health expenditures and inequities.

PubMed

Laokri, Samia; Dramaix-Wilmet, Michèle; Kassa, Ferdinand; Anagonou, Séverin; Dujardin, Bruno

2014-10-01

To inform policy-making, we measured the risk, causes and consequences of catastrophic expenditures for tuberculosis and investigated potential inequities. Between August 2008 and February 2009, a cross-sectional study was conducted among all (245) smear-positive pulmonary tuberculosis patients of six health districts from southern Benin. A standardised survey questionnaire covered the period of time elapsing from onset of tuberculosis symptoms to completion of treatment. Total direct cost exceeding the conventional 10% threshold of annual income was defined as catastrophic and used as principal outcome in a multivariable logistic regression. A sensitivity analysis was performed while varying the thresholds. A pure gradient of direct costs of tuberculosis in relation to income was observed. Incidence (78.1%) and intensity (14.8%) of catastrophic expenditure were high; varying thresholds was insensitive to the intensity. Incurring catastrophic expenditure was independently associated with lower- and middle-income quintiles (adjusted odd ratio (aOR) = 36.2, 95% CI [12.3-106.3] and aOR = 6.4 [2.8-14.6]), adverse pre-diagnosis stage (aOR = 5.4 [2.2-13.3]) and less education (aOR = 4.1[1.9-8.7]). Households incurred important days lost due to TB, indebtedness (37.1%), dissaving (51.0%) and other coping strategies (52.7%). Catastrophic direct costs and substantial indirect and coping costs may persist under the 'free' tuberculosis diagnosis and treatment strategy, as well as inequities in financial hardship. © 2014 John Wiley & Sons Ltd.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Educational and social competencies at 8 years in children with threshold retinopathy of prematurity in the CRYO-ROP multicenter study.

PubMed

Msall, Michael E; Phelps, Dale L; Hardy, Robert J; Dobson, Velma; Quinn, Graham E; Summers, C Gail; Tremont, Michelle R

2004-04-01

To describe the educational status and special education services at 8 years among children who had threshold retinopathy of prematurity (ROP). A prospective study was conducted of a cohort of children who had birth weight of <1251 g and threshold ROP in the Cryotherapy for Retinopathy of Prematurity multicenter study. At age 5.5 years, visual status, functional skills, and social information were obtained. At 8 years, special education classes, developmental disabilities, rehabilitation therapies, and academic and social competencies were determined by questionnaire. Visual status was considered favorable/unfavorable on the basis of the better eye. Of 255 survivors, 216 (85%) were evaluated at both 5.5 and 8 years. Major impairments were significantly more prevalent in children with unfavorable versus favorable visual status: cerebral palsy (39% vs 16%), developmental disability (57% vs 22%), autism (9% vs 1%), and epilepsy (23% vs 3%). Special education services (63% vs 27%), below-grade-level academic performance (84% vs 48%), and school-based rehabilitation services were significantly less common in children with favorable visual status. Favorable visual status, favorable functional ratings at 5.5 years, markers of higher socioeconomic status, and nonblack race were associated with significantly lower rates of both special education placement and below-grade-level academic performance at age 8. On multivariate logistic regression, only favorable visual status and functional status remained significant predictors for decreasing special education placement. Threshold ROP is associated with high rates of developmental, educational, and social challenges in middle childhood; preserved vision was associated with a clear advantage, with more than half of the children with favorable visual status performing at grade level.

Identifying the most appropriate age threshold for TNM stage grouping of well-differentiated thyroid cancer.

PubMed

Hendrickson-Rebizant, J; Sigvaldason, H; Nason, R W; Pathak, K A

2015-08-01

Age is integrated in most risk stratification systems for well-differentiated thyroid cancer (WDTC). The most appropriate age threshold for stage grouping of WDTC is debatable. The objective of this study was to evaluate the best age threshold for stage grouping by comparing multivariable models designed to evaluate the independent impact of various prognostic factors, including age based stage grouping, on the disease specific survival (DSS) of our population-based cohort. Data from population-based thyroid cancer cohort of 2125 consecutive WDTC, diagnosed during 1970-2010, with a median follow-up of 11.5 years, was used to calculate DSS using the Kaplan Meier method. Multivariable analysis with Cox proportional hazard model was used to assess independent impact of different prognostic factors on DSS. The Akaike information criterion (AIC), a measure of statistical model fit, was used to identify the most appropriate age threshold model. Delta AIC, Akaike weight, and evidence ratios were calculated to compare the relative strength of different models. The mean age of the patients was 47.3 years. DSS of the cohort was 95.6% and 92.8% at 10 and 20 years respectively. A threshold of 55 years, with the lowest AIC, was identified as the best model. Akaike weight indicated an 85% chance that this age threshold is the best among the compared models, and is 16.8 times more likely to be the best model as compared to a threshold of 45 years. The age threshold of 55 years was found to be the best for TNM stage grouping. Copyright © 2015 Elsevier Ltd. All rights reserved.

Threshold regression to accommodate a censored covariate.

PubMed

Qian, Jing; Chiou, Sy Han; Maye, Jacqueline E; Atem, Folefac; Johnson, Keith A; Betensky, Rebecca A

2018-06-22

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring. Threshold regression methods allow for immediate testing of the significance of the effect of a censored covariate. In addition, they provide for unbiased estimation of the regression coefficient of the censored covariate. We derive the asymptotic properties of the resulting estimators under mild regularity conditions. Simulations demonstrate that the proposed estimators have good finite-sample performance, and often offer improved efficiency over existing methods. We also derive a principled method for selection of the threshold. We illustrate the approach in application to an Alzheimer's disease study that investigated brain amyloid levels in older individuals, as measured through positron emission tomography scans, as a function of maternal age of dementia onset, with adjustment for other covariates. We have developed an R package, censCov, for implementation of our method, available at CRAN. © 2018, The International Biometric Society.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

PubMed

Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

2018-03-01

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables. © 2017 by the Ecological Society of America.

Weather sensitivity for zoo visitation in Toronto, Canada: a quantitative analysis of historical data

NASA Astrophysics Data System (ADS)

Hewer, Micah J.; Gough, William A.

2016-11-01

Based on a case study of the Toronto Zoo (Canada), multivariate regression analysis, involving both climatic and social variables, was employed to assess the relationship between daily weather and visitation. Zoo visitation was most sensitive to weather variability during the shoulder season, followed by the off-season and, then, the peak season. Temperature was the most influential weather variable in relation to zoo visitation, followed by precipitation and, then, wind speed. The intensity and direction of the social and climatic variables varied between seasons. Temperatures exceeding 26 °C during the shoulder season and 28 °C during the peak season suggested a behavioural threshold associated with zoo visitation, with conditions becoming too warm for certain segments of the zoo visitor market, causing visitor numbers to decline. Even light amounts of precipitation caused average visitor numbers to decline by nearly 50 %. Increasing wind speeds also demonstrated a negative influence on zoo visitation.

Generalized Hyperalgesia in Children and Adults Diagnosed With Hypermobility Syndrome and Ehlers-Danlos Syndrome Hypermobility Type: A Discriminative Analysis.

PubMed

Scheper, M C; Pacey, V; Rombaut, L; Adams, R D; Tofts, L; Calders, P; Nicholson, L L; Engelbert, R H H

2017-03-01

Lowered pressure-pain thresholds have been demonstrated in adults with Ehlers-Danlos syndrome hypermobility type (EDS-HT), but whether these findings are also present in children is unclear. Therefore, the objectives of the study were to determine whether generalized hyperalgesia is present in children with hypermobility syndrome (HMS)/EDS-HT, explore potential differences in pressure-pain thresholds between children and adults with HMS/EDS-HT, and determine the discriminative value of generalized hyperalgesia. Patients were classified in 1 of 3 groups: HMS/EDS-HT, hypermobile (Beighton score ≥4 of 9), and healthy controls. Descriptive data of age, sex, body mass index, Beighton score, skin laxity, and medication usage were collected. Generalized hyperalgesia was quantified by the average pressure-pain thresholds collected from 12 locations. Confounders collected were pain locations/intensity, fatigue, and psychological distress. Comparisons between children with HMS/EDS-HT and normative values, between children and adults with HMS/EDS-HT, and corrected confounders were analyzed with multivariate analysis of covariance. The discriminative value of generalized hyperalgesia employed to differentiate between HMS/EDS-HT, hypermobility, and controls was quantified with logistic regression. Significantly lower pressure-pain thresholds were found in children with HMS/EDS-HT compared to normative values (range -22.0% to -59.0%; P ≤ 0.05). When applying a threshold of 30.8 N/cm 2 for males and 29.0 N/cm 2 for females, the presence of generalized hyperalgesia discriminated between individuals with HMS/EDS-HT, hypermobility, and healthy controls (odds ratio 6.0). Children and adults with HMS/EDS-HT are characterized by hypermobility, chronic pain, and generalized hyperalgesia. The presence of generalized hyperalgesia may indicate involvement of the central nervous system in the development of chronic pain. © 2016, American College of Rheumatology.

Prognostic value of inflammation-based scores in patients with osteosarcoma

PubMed Central

Liu, Bangjian; Huang, Yujing; Sun, Yuanjue; Zhang, Jianjun; Yao, Yang; Shen, Zan; Xiang, Dongxi; He, Aina

2016-01-01

Systemic inflammation responses have been associated with cancer development and progression. C-reactive protein (CRP), Glasgow prognostic score (GPS), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and neutrophil-platelet score (NPS) have been shown to be independent risk factors in various types of malignant tumors. This retrospective analysis of 162 osteosarcoma cases was performed to estimate their predictive value of survival in osteosarcoma. All statistical analyses were performed by SPSS statistical software. Receiver operating characteristic (ROC) analysis was generated to set optimal thresholds; area under the curve (AUC) was used to show the discriminatory abilities of inflammation-based scores; Kaplan-Meier analysis was performed to plot the survival curve; cox regression models were employed to determine the independent prognostic factors. The optimal cut-off points of NLR, PLR, and LMR were 2.57, 123.5 and 4.73, respectively. GPS and NLR had a markedly larger AUC than CRP, PLR and LMR. High levels of CRP, GPS, NLR, PLR, and low level of LMR were significantly associated with adverse prognosis (P < 0.05). Multivariate Cox regression analyses revealed that GPS, NLR, and occurrence of metastasis were top risk factors associated with death of osteosarcoma patients. PMID:28008988

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

Digital vibration threshold testing and ergonomic stressors in automobile manufacturing workers: a cross-sectional assessment.

PubMed

Gold, J E; Punnett, L; Cherniack, M; Wegman, D H

2005-01-01

Upper extremity musculoskeletal disorders (UEMSDs) comprise a large proportion of work-related illnesses in the USA. Physical risk factors including manual force and segmental vibration have been associated with UEMSDs. Reduced sensitivity to vibration in the fingertips (a function of nerve integrity) has been found in those exposed to segmental vibration, to hand force, and in office workers. The objective of this study was to determine whether an association exists between digital vibration thresholds (VTs) and exposure to ergonomic stressors in automobile manufacturing. Interviews and physical examinations were conducted in a cross-sectional survey of workers (n = 1174). In multivariable robust regression modelling, associations with workers' estimates of ergonomic stressors stratified on tool use were determined. VTs were separately associated with hand force, vibration as felt through the floor (whole body vibration), and with an index of multiple exposures in both tool users and non-tool users. Additional associations with contact stress and awkward upper extremity postures were found in tool users. Segmental vibration was not associated with VTs. Further epidemiologic and laboratory studies are needed to confirm the associations found. The association with self-reported whole body vibration exposure suggests a possible sympathetic nervous system effect, which remains to be explored.

Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus)

USGS Publications Warehouse

Semmens, Brice X.; Semmens, Darius J.; Thogmartin, Wayne E.; Wiederholt, Ruscena; Lopez-Hoffman, Laura; Diffendorfer, James E.; Pleasants, John M.; Oberhauser, Karen S.; Taylor, Orley R.

2016-01-01

The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch’s multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11–57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers, and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014–15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines, and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability.

Examining the impacts of increased corn production on ...

EPA Pesticide Factsheets

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥ 5 mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additio

Risk assessment of student performance in the International Foundations of Medicine Clinical Science Examination by the use of statistical modeling.

PubMed

David, Michael C; Eley, Diann S; Schafer, Jennifer; Davies, Leo

2016-01-01

The primary aim of this study was to assess the predictive validity of cumulative grade point average (GPA) for performance in the International Foundations of Medicine (IFOM) Clinical Science Examination (CSE). A secondary aim was to develop a strategy for identifying students at risk of performing poorly in the IFOM CSE as determined by the National Board of Medical Examiners' International Standard of Competence. Final year medical students from an Australian university medical school took the IFOM CSE as a formative assessment. Measures included overall IFOM CSE score as the dependent variable, cumulative GPA as the predictor, and the factors age, gender, year of enrollment, international or domestic status of student, and language spoken at home as covariates. Multivariable linear regression was used to measure predictor and covariate effects. Optimal thresholds of risk assessment were based on receiver-operating characteristic (ROC) curves. Cumulative GPA (nonstandardized regression coefficient [B]: 81.83; 95% confidence interval [CI]: 68.13 to 95.53) and international status (B: -37.40; 95% CI: -57.85 to -16.96) from 427 students were found to be statistically associated with increased IFOM CSE performance. Cumulative GPAs of 5.30 (area under ROC [AROC]: 0.77; 95% CI: 0.72 to 0.82) and 4.90 (AROC: 0.72; 95% CI: 0.66 to 0.78) were identified as being thresholds of significant risk for domestic and international students, respectively. Using cumulative GPA as a predictor of IFOM CSE performance and accommodating for differences in international status, it is possible to identify students who are at risk of failing to satisfy the National Board of Medical Examiners' International Standard of Competence.

Applications of stochastic models and geostatistical analyses to study sources and spatial patterns of soil heavy metals in a metalliferous industrial district of China.

PubMed

Zhong, Buqing; Liang, Tao; Wang, Lingqing; Li, Kexin

2014-08-15

An extensive soil survey was conducted to study pollution sources and delineate contamination of heavy metals in one of the metalliferous industrial bases, in the karst areas of southwest China. A total of 597 topsoil samples were collected and the concentrations of five heavy metals, namely Cd, As (metalloid), Pb, Hg and Cr were analyzed. Stochastic models including a conditional inference tree (CIT) and a finite mixture distribution model (FMDM) were applied to identify the sources and partition the contribution from natural and anthropogenic sources for heavy metal in topsoils of the study area. Regression trees for Cd, As, Pb and Hg were proved to depend mostly on indicators of anthropogenic activities such as industrial type and distance from urban area, while the regression tree for Cr was found to be mainly influenced by the geogenic characteristics. The FMDM analysis showed that the geometric means of modeled background values for Cd, As, Pb, Hg and Cr were close to their background values previously reported in the study area, while the contamination of Cd and Hg were widespread in the study area, imposing potentially detrimental effects on organisms through the food chain. Finally, the probabilities of single and multiple heavy metals exceeding the threshold values derived from the FMDM were estimated using indicator kriging (IK) and multivariate indicator kriging (MVIK). The high probabilities exceeding the thresholds of heavy metals were associated with metalliferous production and atmospheric deposition of heavy metals transported from the urban and industrial areas. Geostatistics coupled with stochastic models provide an effective way to delineate multiple heavy metal pollution to facilitate improved environmental management. Copyright © 2014 Elsevier B.V. All rights reserved.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

High-permeability region size on perfusion CT predicts hemorrhagic transformation after intravenous thrombolysis in stroke

PubMed Central

Puig, Josep; Blasco, Gerard; Daunis-i-Estadella, Pepus; van Eendendburg, Cecile; Carrillo-García, María; Aboud, Carlos; Hernández-Pérez, María; Serena, Joaquín; Biarnés, Carles; Nael, Kambiz; Liebeskind, David S.; Thomalla, Götz; Menon, Bijoy K.; Demchuk, Andrew; Wintermark, Max; Pedraza, Salvador

2017-01-01

Objective Blood-brain barrier (BBB) permeability has been proposed as a predictor of hemorrhagic transformation (HT) after tissue plasminogen activator (tPA) administration; however, the reliability of perfusion computed tomography (PCT) permeability imaging for predicting HT is uncertain. We aimed to determine the performance of high-permeability region size on PCT (HPrs-PCT) in predicting HT after intravenous tPA administration in patients with acute stroke. Methods We performed a multimodal CT protocol (non-contrast CT, PCT, CT angiography) to prospectively study patients with middle cerebral artery occlusion treated with tPA within 4.5 hours of symptom onset. HT was graded at 24 hours using the European-Australasian Acute Stroke Study II criteria. ROC curves selected optimal volume threshold, and multivariate logistic regression analysis identified predictors of HT. Results The study included 156 patients (50% male, median age 75.5 years). Thirty-seven (23,7%) developed HT [12 (7,7%), parenchymal hematoma type 2 (PH-2)]. At admission, patients with HT had lower platelet values, higher NIHSS scores, increased ischemic lesion volumes, larger HPrs-PCT, and poorer collateral status. The negative predictive value of HPrs-PCT at a threshold of 7mL/100g/min was 0.84 for HT and 0.93 for PH-2. The multiple regression analysis selected HPrs-PCT at 7mL/100g/min combined with platelets and baseline NIHSS score as the best model for predicting HT (AUC 0.77). HPrs-PCT at 7mL/100g/min was the only independent predictor of PH-2 (OR 1, AUC 0.68, p = 0.045). Conclusions HPrs-PCT can help predict HT after tPA, and is particularly useful in identifying patients at low risk of developing HT. PMID:29182658

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

Evaluation of Pressure Pain Threshold as a Measure of Perceived Stress and High Job Strain.

PubMed

Hven, Lisbeth; Frost, Poul; Bonde, Jens Peter Ellekilde

2017-01-01

To investigate whether pressure pain threshold (PPT), determined by pressure algometry, can be used as an objective measure of perceived stress and job strain. We used cross-sectional base line data collected during 1994 to 1995 within the Project on Research and Intervention in Monotonous work (PRIM), which included 3123 employees from a variety of Danish companies. Questionnaire data included 18 items on stress symptoms, 23 items from the Karasek scale on job strain, and information on discomfort in specified anatomical regions was also collected. Clinical examinations included pressure pain algometry measurements of PPT on the trapezius and supraspinatus muscles and the tibia. Associations of stress symptoms and job strain with PPT of each site was analyzed for men and women separately with adjustment for age body mass index, and discomfort in the anatomical region closest to the point of pressure algometry using multivariable linear regression. We found significant inverse associations between perceived stress and PPT in both genders in models adjusting for age and body mass index: the higher level of perceived stress, the lower the threshold. For job strain, associations were weaker and only present in men. In men all associations were attenuated when adjusting for reported discomfort in regions close to the site of pressure algometry. The distributions of PPT among stressed and non-stressed persons were strongly overlapping. Despite significant associations between perceived stress and PPT, the discriminative capability of PPT to distinguish individuals with and without stress is low. PPT measured by pressure algometry seems not applicable as a diagnostic tool of a state of mental stress.

The prehospital intravenous access assessment: a prospective study on intravenous access failure and access delay in prehospital emergency medicine.

PubMed

Prottengeier, Johannes; Albermann, Matthias; Heinrich, Sebastian; Birkholz, Torsten; Gall, Christine; Schmidt, Joachim

2016-12-01

Intravenous access in prehospital emergency care allows for early administration of medication and extended measures such as anaesthesia. Cannulation may, however, be difficult, and failure and resulting delay in treatment and transport may have negative effects on the patient. Therefore, our study aims to perform a concise assessment of the difficulties of prehospital venous cannulation. We analysed 23 candidate predictor variables on peripheral venous cannulations in terms of cannulation failure and exceedance of a 2 min time threshold. Multivariate logistic regression models were fitted for variables of predictive value (P<0.25) and evaluated by the area under the curve (AUC>0.6) of their respective receiver operating characteristic curve. A total of 762 intravenous cannulations were enroled. In all, 22% of punctures failed on the first attempt and 13% of punctures exceeded 2 min. Model selection yielded a three-factor model (vein visibility without tourniquet, vein palpability with tourniquet and insufficient ambient lighting) of fair accuracy for the prediction of puncture failure (AUC=0.76) and a structurally congruent model of four factors (failure model factors plus vein visibility with tourniquet) for the exceedance of the 2 min threshold (AUC=0.80). Our study offers a simple assessment to identify cases of difficult intravenous access in prehospital emergency care. Of the numerous factors subjectively perceived as possibly exerting influences on cannulation, only the universal - not exclusive to emergency care - factors of lighting, vein visibility and palpability proved to be valid predictors of cannulation failure and exceedance of a 2 min threshold.

Prediction of spatially explicit rainfall intensity-duration thresholds for post-fire debris-flow generation in the western United States

NASA Astrophysics Data System (ADS)

Staley, Dennis; Negri, Jacquelyn; Kean, Jason

2016-04-01

Population expansion into fire-prone steeplands has resulted in an increase in post-fire debris-flow risk in the western United States. Logistic regression methods for determining debris-flow likelihood and the calculation of empirical rainfall intensity-duration thresholds for debris-flow initiation represent two common approaches for characterizing hazard and reducing risk. Logistic regression models are currently being used to rapidly assess debris-flow hazard in response to design storms of known intensities (e.g. a 10-year recurrence interval rainstorm). Empirical rainfall intensity-duration thresholds comprise a major component of the United States Geological Survey (USGS) and the National Weather Service (NWS) debris-flow early warning system at a regional scale in southern California. However, these two modeling approaches remain independent, with each approach having limitations that do not allow for synergistic local-scale (e.g. drainage-basin scale) characterization of debris-flow hazard during intense rainfall. The current logistic regression equations consider rainfall a unique independent variable, which prevents the direct calculation of the relation between rainfall intensity and debris-flow likelihood. Regional (e.g. mountain range or physiographic province scale) rainfall intensity-duration thresholds fail to provide insight into the basin-scale variability of post-fire debris-flow hazard and require an extensive database of historical debris-flow occurrence and rainfall characteristics. Here, we present a new approach that combines traditional logistic regression and intensity-duration threshold methodologies. This method allows for local characterization of both the likelihood that a debris-flow will occur at a given rainfall intensity, the direct calculation of the rainfall rates that will result in a given likelihood, and the ability to calculate spatially explicit rainfall intensity-duration thresholds for debris-flow generation in recently burned areas. Our approach synthesizes the two methods by incorporating measured rainfall intensity into each model variable (based on measures of topographic steepness, burn severity and surface properties) within the logistic regression equation. This approach provides a more realistic representation of the relation between rainfall intensity and debris-flow likelihood, as likelihood values asymptotically approach zero when rainfall intensity approaches 0 mm/h, and increase with more intense rainfall. Model performance was evaluated by comparing predictions to several existing regional thresholds. The model, based upon training data collected in southern California, USA, has proven to accurately predict rainfall intensity-duration thresholds for other areas in the western United States not included in the original training dataset. In addition, the improved logistic regression model shows promise for emergency planning purposes and real-time, site-specific early warning. With further validation, this model may permit the prediction of spatially-explicit intensity-duration thresholds for debris-flow generation in areas where empirically derived regional thresholds do not exist. This improvement would permit the expansion of the early-warning system into other regions susceptible to post-fire debris flow.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Treesearch

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

Can Preoperative Patient-reported Outcome Measures Be Used to Predict Meaningful Improvement in Function After TKA?

PubMed

Berliner, Jonathan L; Brodke, Dane J; Chan, Vanessa; SooHoo, Nelson F; Bozic, Kevin J

2017-01-01

Despite the overall effectiveness of total knee arthroplasty (TKA), a subset of patients do not experience expected improvements in pain, physical function, and quality of life as documented by patient-reported outcome measures (PROMs), which assess a patient's physical and emotional health and pain. It is therefore important to develop preoperative tools capable of identifying patients unlikely to improve by a clinically important margin after surgery. The purpose of this study was to determine if an association exists between preoperative PROM scores and patients' likelihood of experiencing a clinically meaningful change in function 1 year after TKA. A retrospective study design was used to evaluate preoperative and 1-year postoperative Knee injury and Osteoarthritis Outcome Score (KOOS) and SF-12 version 2 (SF12v2) scores from 562 patients who underwent primary unilateral TKA. This cohort represented 75% of the 750 patients who underwent surgery during that time period; a total of 188 others (25%) either did not complete PROM scores at the designated times or were lost to follow-up. Minimum clinically important differences (MCIDs) were calculated for each PROM using a distribution-based method and were used to define meaningful clinical improvement. MCID values for KOOS and SF12v2 physical component summary (PCS) scores were calculated to be 10 and 5, respectively. A receiver operating characteristic analysis was used to determine threshold values for preoperative KOOS and SF12v2 PCS scores and their respective predictive abilities. Threshold values defined the point after which the likelihood of clinically meaningful improvement began to diminish. Multivariate regression was used to control for the effect of preoperative mental and emotional health, patient attributes quantified by SF12v2 mental component summary (MCS) scores, on patients' likelihood of experiencing meaningful improvement in function after surgery. Threshold values for preoperative KOOS and SF12v2 PCS scores were a maximum of 58 (area under the curve [AUC], 0.76; p < 0.001) and 34 (AUC, 0.65; p < 0.001), respectively. Patients scoring above these thresholds, indicating better preoperative function, were less likely to experience a clinically meaningful improvement in function after TKA. When accounting for mental and emotional health with a multivariate analysis, the predictive ability of both KOOS and SF12v2 PCS threshold values improved (AUCs increased to 0.80 and 0.71, respectively). Better preoperative mental and emotional health, as reflected by a higher MCS score, resulted in higher threshold values for KOOS and SF12v2 PCS. We identified preoperative PROM threshold values that are associated with clinically meaningful improvements in functional outcome after TKA. Patients with preoperative KOOS or SF12v2 PCS scores above the defined threshold values have a diminishing probability of experiencing clinically meaningful improvement after TKA. Patients with worse baseline mental and emotional health (as defined by SF12v2 MCS score) have a lower probability of experiencing clinically important levels of functional improvement after surgery. The results of this study are directly applicable to patient-centered informed decision-making tools and may be used to facilitate discussions with patients regarding the expected benefit after TKA. Level III, prognostic study.

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Practical determination of aortic valve calcium volume score on contrast-enhanced computed tomography prior to transcatheter aortic valve replacement and impact on paravalvular regurgitation: Elucidating optimal threshold cutoffs.

PubMed

Bettinger, Nicolas; Khalique, Omar K; Krepp, Joseph M; Hamid, Nadira B; Bae, David J; Pulerwitz, Todd C; Liao, Ming; Hahn, Rebecca T; Vahl, Torsten P; Nazif, Tamim M; George, Isaac; Leon, Martin B; Einstein, Andrew J; Kodali, Susheel K

The threshold for the optimal computed tomography (CT) number in Hounsfield Units (HU) to quantify aortic valvular calcium on contrast-enhanced scans has not been standardized. Our aim was to find the most accurate threshold to predict paravalvular regurgitation (PVR) after transcatheter aortic valve replacement (TAVR). 104 patients who underwent TAVR with the CoreValve prosthesis were studied retrospectively. Luminal attenuation (LA) in HU was measured at the level of the aortic annulus. Calcium volume score for the aortic valvular complex was measured using 6 threshold cutoffs (650 HU, 850 HU, LA × 1.25, LA × 1.5, LA+50, LA+100). Receiver-operating characteristic (ROC) analysis was performed to assess the predictive value for > mild PVR (n = 16). Multivariable analysis was performed to determine the accuracy to predict > mild PVR after adjustment for depth and perimeter oversizing. ROC analysis showed lower area under the curve (AUC) values for fixed threshold cutoffs (650 or 850 HU) compared to thresholds relative to LA. The LA+100 threshold had the highest AUC (0.81), and AUC was higher than all studied protocols, other than the LA x 1.25 and LA + 50 protocols, where the difference approached statistical significance (p = 0.05, and 0.068, respectively). Multivariable analysis showed calcium volume determined by the LAx1.25, LAx1.5, LA+50, and LA+ 100 HU protocols to independently predict PVR. Calcium volume scoring thresholds which are relative to LA are more predictive of PVR post-TAVR than those which use fixed cutoffs. A threshold of LA+100 HU had the highest predictive value. Copyright © 2017 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Clinical performance of the Prostate Health Index (PHI) for the prediction of prostate cancer in obese men: data from the PROMEtheuS project, a multicentre European prospective study.

PubMed

Abrate, Alberto; Lazzeri, Massimo; Lughezzani, Giovanni; Buffi, Nicolòmaria; Bini, Vittorio; Haese, Alexander; de la Taille, Alexandre; McNicholas, Thomas; Redorta, Joan Palou; Gadda, Giulio M; Lista, Giuliana; Kinzikeeva, Ella; Fossati, Nicola; Larcher, Alessandro; Dell'Oglio, Paolo; Mistretta, Francesco; Freschi, Massimo; Guazzoni, Giorgio

2015-04-01

To test serum prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA), p2PSA/free PSA (%p2PSA) and Prostate Health Index (PHI) accuracy in predicting prostate cancer in obese men and to test whether PHI is more accurate than PSA in predicting prostate cancer in obese patients. The analysis consisted of a nested case-control study from the pro-PSA Multicentric European Study (PROMEtheuS) project. The study is registered at http://www.controlled-trials.com/ISRCTN04707454. The primary outcome was to test sensitivity, specificity and accuracy (clinical validity) of serum p2PSA, %p2PSA and PHI, in determining prostate cancer at prostate biopsy in obese men [body mass index (BMI) ≥30 kg/m(2) ], compared with total PSA (tPSA), free PSA (fPSA) and fPSA/tPSA ratio (%fPSA). The number of avoidable prostate biopsies (clinical utility) was also assessed. Multivariable logistic regression models were complemented by predictive accuracy analysis and decision-curve analysis. Of the 965 patients, 383 (39.7%) were normal weight (BMI <25 kg/m(2) ), 440 (45.6%) were overweight (BMI 25-29.9 kg/m(2) ) and 142 (14.7%) were obese (BMI ≥30 kg/m(2) ). Among obese patients, prostate cancer was found in 65 patients (45.8%), with a higher percentage of Gleason score ≥7 diseases (67.7%). PSA, p2PSA, %p2PSA and PHI were significantly higher, and %fPSA significantly lower in patients with prostate cancer (P < 0.001). In multivariable logistic regression models, PHI significantly increased accuracy of the base multivariable model by 8.8% (P = 0.007). At a PHI threshold of 35.7, 46 (32.4%) biopsies could have been avoided. In obese patients, PHI is significantly more accurate than current tests in predicting prostate cancer. © 2014 The Authors. BJU International © 2014 BJU International.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Influence of Injury Risk Thresholds on the Performance of an Algorithm to Predict Crashes with Serious Injuries

PubMed Central

Bahouth, George; Digges, Kennerly; Schulman, Carl

2012-01-01

This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20. PMID:23169132

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Inclusion of Exercise Intensities Above the Lactate Threshold in VO2/Running Speed Regression Does not Improve the Precision of Accumulated Oxygen Deficit Estimation in Endurance-Trained Runners

PubMed Central

Reis, Victor M.; Silva, António J.; Ascensão, António; Duarte, José A.

2005-01-01

The present study intended to verify if the inclusion of intensities above lactate threshold (LT) in the VO2/running speed regression (RSR) affects the estimation error of accumulated oxygen deficit (AOD) during a treadmill running performed by endurance-trained subjects. Fourteen male endurance-trained runners performed a sub maximal treadmill running test followed by an exhaustive supra maximal test 48h later. The total energy demand (TED) and the AOD during the supra maximal test were calculated from the RSR established on first testing. For those purposes two regressions were used: a complete regression (CR) including all available sub maximal VO2 measurements and a sub threshold regression (STR) including solely the VO2 values measured during exercise intensities below LT. TED mean values obtained with CR and STR were not significantly different under the two conditions of analysis (177.71 ± 5.99 and 174.03 ± 6.53 ml·kg-1, respectively). Also the mean values of AOD obtained with CR and STR did not differ under the two conditions (49.75 ± 8.38 and 45.8 9 ± 9.79 ml·kg-1, respectively). Moreover, the precision of those estimations was also similar under the two procedures. The mean error for TED estimation was 3.27 ± 1.58 and 3.41 ± 1.85 ml·kg-1 (for CR and STR, respectively) and the mean error for AOD estimation was 5.03 ± 0.32 and 5.14 ± 0.35 ml·kg-1 (for CR and STR, respectively). The results indicated that the inclusion of exercise intensities above LT in the RSR does not improve the precision of the AOD estimation in endurance-trained runners. However, the use of STR may induce an underestimation of AOD comparatively to the use of CR. Key Points It has been suggested that the inclusion of exercise intensities above the lactate threshold in the VO2/power regression can significantly affect the estimation of the energy cost and, thus, the estimation of the AOD. However data on the precision of those AOD measurements is rarely provided. We have evaluated the effects of the inclusion of those exercise intensities on the AOD precision. The results have indicated that the inclusion of exercise intensities above the lactate threshold in the VO2/running speed regression does not improve the precision of AOD estimation in endurance-trained runners. However, the use of sub threshold regressions may induce an underestimation of AOD comparatively to the use of complete regressions. PMID:24501560

A piecewise regression approach for determining biologically relevant hydraulic thresholds for the protection of fishes at river infrastructure.

PubMed

Boys, C A; Robinson, W; Miller, B; Pflugrath, B; Baumgartner, L J; Navarro, A; Brown, R; Deng, Z

2016-05-01

A piecewise regression approach was used to objectively quantify barotrauma injury thresholds in two physoclistous species, Murray cod Maccullochella peelii and silver perch Bidyanus bidyanus, following simulated infrastructure passage in a barometric chamber. The probability of injuries such as swimbladder rupture, exophthalmia and haemorrhage, and emphysema in various organs increased as the ratio between the lowest exposure pressure and the acclimation pressure (ratio of pressure change, R(NE:A) ) reduced. The relationship was typically non-linear and piecewise regression was able to quantify thresholds in R(NE:A) that once exceeded resulted in a substantial increase in barotrauma injury. Thresholds differed among injury types and between species but by applying a multispecies precautionary principle, the maintenance of exposure pressures at river infrastructure above 70% of acclimation pressure (R(NE:A) of 0·7) should protect downstream migrating juveniles of these two physoclistous species sufficiently. These findings have important implications for determining the risk posed by current infrastructures and informing the design and operation of new ones. © 2016 The Fisheries Society of the British Isles.

Forecasting the probability of future groundwater levels declining below specified low thresholds in the conterminous U.S.

USGS Publications Warehouse

Dudley, Robert W.; Hodgkins, Glenn A.; Dickinson, Jesse

2017-01-01

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater-level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness-of-fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month-to-month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low-threshold events. We identified challenges in deriving probabilistic-forecasting models and possible approaches for addressing those challenges.

The self-perception of dyspnoea threshold during the 6-min walk test: a good alternative to estimate the ventilatory threshold in chronic obstructive pulmonary disease.

PubMed

Couillard, Annabelle; Tremey, Emilie; Prefaut, Christian; Varray, Alain; Heraud, Nelly

2016-12-01

To determine and/or adjust exercise training intensity for patients when the cardiopulmonary exercise test is not accessible, the determination of dyspnoea threshold (defined as the onset of self-perceived breathing discomfort) during the 6-min walk test (6MWT) could be a good alternative. The aim of this study was to evaluate the feasibility and reproducibility of self-perceived dyspnoea threshold and to determine whether a useful equation to estimate ventilatory threshold from self-perceived dyspnoea threshold could be derived. A total of 82 patients were included and performed two 6MWTs, during which they raised a hand to signal self-perceived dyspnoea threshold. The reproducibility in terms of heart rate (HR) was analysed. On a subsample of patients (n=27), a stepwise regression analysis was carried out to obtain a predictive equation of HR at ventilatory threshold measured during a cardiopulmonary exercise test estimated from HR at self-perceived dyspnoea threshold, age and forced expiratory volume in 1 s. Overall, 80% of patients could identify self-perceived dyspnoea threshold during the 6MWT. Self-perceived dyspnoea threshold was reproducibly expressed in HR (coefficient of variation=2.8%). A stepwise regression analysis enabled estimation of HR at ventilatory threshold from HR at self-perceived dyspnoea threshold, age and forced expiratory volume in 1 s (adjusted r=0.79, r=0.63, and relative standard deviation=9.8 bpm). This study shows that a majority of patients with chronic obstructive pulmonary disease can identify a self-perceived dyspnoea threshold during the 6MWT. This HR at the dyspnoea threshold is highly reproducible and enable estimation of the HR at the ventilatory threshold.

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and

Alkaline phosphatase: the next independent predictor of the poor 90-day outcome in alcoholic hepatitis.

PubMed

Kasztelan-Szczerbinska, Beata; Slomka, Maria; Celinski, Krzysztof; Szczerbinski, Mariusz

2013-01-01

Determination of risk factors relevant to 90-day prognosis in AH. Comparison of the conventional prognostic models such as Maddrey's modified discriminant function (mDF) and Child-Pugh-Turcotte (CPT) score with newer ones: the Glasgow Alcoholic Hepatitis Score (GAHS); Age, Bilirubin, INR, Creatinine (ABIC) score, Model for End-Stage Liver Disease (MELD), and MELD-Na in the death prediction. The clinical and laboratory variables obtained at admission were assessed. The mDF, CPT, GAHS, ABIC, MELD, and MELD-Na scores' different areas under the curve (AUCs) and the best threshold values were compared. Logistic regression was used to assess predictors of the 90-day outcome. One hundred sixteen pts fulfilled the inclusion criteria. Twenty (17.4%) pts died and one underwent orthotopic liver transplantation (OLT) within 90 days of follow-up. No statistically significant differences in the models' performances were found. Multivariate logistic regression identified CPT score, alkaline phosphatase (AP) level higher than 1.5 times the upper limit of normal (ULN), and corticosteroids (CS) nonresponse as independent predictors of mortality. The CPT score, AP > 1.5 ULN, and the CS nonresponse had an independent impact on the 90-day survival in AH. Accuracy of all studied scoring systems was comparable.

Deterioration of Speech Recognition Ability Over a Period of 5 Years in Adults Ages 18 to 70 Years: Results of the Dutch Online Speech-in-Noise Test.

PubMed

Stam, Mariska; Smits, Cas; Twisk, Jos W R; Lemke, Ulrike; Festen, Joost M; Kramer, Sophia E

2015-01-01

The first aim of the present study was to determine the change in speech recognition in noise over a period of 5 years in participants ages 18 to 70 years at baseline. The second aim was to investigate whether age, gender, educational level, the level of initial speech recognition in noise, and reported chronic conditions were associated with a change in speech recognition in noise. The baseline and 5-year follow-up data of 427 participants with and without hearing impairment participating in the National Longitudinal Study on Hearing (NL-SH) were analyzed. The ability to recognize speech in noise was measured twice with the online National Hearing Test, a digit-triplet speech-in-noise test. Speech-reception-threshold in noise (SRTn) scores were calculated, corresponding to 50% speech intelligibility. Unaided SRTn scores obtained with the same transducer (headphones or loudspeakers) at both test moments were included. Changes in SRTn were calculated as a raw shift (T1 - T0) and an adjusted shift for regression towards the mean. Paired t tests and multivariable linear regression analyses were applied. The mean increase (i.e., deterioration) in SRTn was 0.38-dB signal-to-noise ratio (SNR) over 5 years (p < 0.001). Results of the multivariable regression analyses showed that the age group of 50 to 59 years had a significantly larger deterioration in SRTn compared with the age group of 18 to 39 years (raw shift: beta: 0.64-dB SNR; 95% confidence interval: 0.07-1.22; p = 0.028, adjusted for initial speech recognition level - adjusted shift: beta: 0.82-dB SNR; 95% confidence interval: 0.27-1.34; p = 0.004). Gender, educational level, and the number of chronic conditions were not associated with a change in SRTn over time. No significant differences in increase of SRTn were found between the initial levels of speech recognition (i.e., good, insufficient, or poor) when taking into account the phenomenon regression towards the mean. The study results indicate that hearing deterioration of speech recognition in noise over 5 years can also be detected in adults ages 18 to 70 years. This rather small numeric change might represent a relevant impact on an individual's ability to understand speech in everyday life.

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

USGS Publications Warehouse

Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam

2012-01-01

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.

Identifying an optimal cutpoint value for the diagnosis of hypertriglyceridemia in the nonfasting state

PubMed Central

White, Khendi T.; Moorthy, M.V.; Akinkuolie, Akintunde O.; Demler, Olga; Ridker, Paul M; Cook, Nancy R.; Mora, Samia

2015-01-01

Background Nonfasting triglycerides are similar to or superior to fasting triglycerides at predicting cardiovascular events. However, diagnostic cutpoints are based on fasting triglycerides. We examined the optimal cutpoint for increased nonfasting triglycerides. Methods Baseline nonfasting (<8 hours since last meal) samples were obtained from 6,391 participants in the Women’s Health Study, followed prospectively for up to 17 years. The optimal diagnostic threshold for nonfasting triglycerides, determined by logistic regression models using c-statistics and Youden index (sum of sensitivity and specificity minus one), was used to calculate hazard ratios for incident cardiovascular events. Performance was compared to thresholds recommended by the American Heart Association (AHA) and European guidelines. Results The optimal threshold was 175 mg/dL (1.98 mmol/L), corresponding to a c-statistic of 0.656 that was statistically better than the AHA cutpoint of 200 mg/dL (c-statistic of 0.628). For nonfasting triglycerides above and below 175 mg/dL, adjusting for age, hypertension, smoking, hormone use, and menopausal status, the hazard ratio for cardiovascular events was 1.88 (95% CI, 1.52–2.33, P<0.001), and for triglycerides measured at 0–4 and 4–8 hours since last meal, hazard ratios (95%CIs) were 2.05 (1.54– 2.74) and 1.68 (1.21–2.32), respectively. Performance of this optimal cutpoint was validated using ten-fold cross-validation and bootstrapping of multivariable models that included standard risk factors plus total and HDL cholesterol, diabetes, body-mass index, and C-reactive protein. Conclusions In this study of middle aged and older apparently healthy women, we identified a diagnostic threshold for nonfasting hypertriglyceridemia of 175 mg/dL (1.98 mmol/L), with the potential to more accurately identify cases than the currently recommended AHA cutpoint. PMID:26071491

Heterogeneous Effects of Fructose on Blood Lipids in Individuals With Type 2 Diabetes

PubMed Central

Sievenpiper, John L.; Carleton, Amanda J.; Chatha, Sheena; Jiang, Henry Y.; de Souza, Russell J.; Beyene, Joseph; Kendall, Cyril W.C.; Jenkins, David J.A.

2009-01-01

OBJECTIVE Because of blood lipid concerns, diabetes associations discourage fructose at high intakes. To quantify the effect of fructose on blood lipids in diabetes, we conducted a systematic review and meta-analysis of experimental clinical trials investigating the effect of isocaloric fructose exchange for carbohydrate on triglycerides, total cholesterol, LDL cholesterol, and HDL cholesterol in type 1 and 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE, EMBASE, CINAHL, and the Cochrane Library for relevant trials of ≥7 days. Data were pooled by the generic inverse variance method and expressed as standardized mean differences with 95% CI. Heterogeneity was assessed by χ2 tests and quantified by I2. Meta-regression models identified dose threshold and independent predictors of effects. RESULTS Sixteen trials (236 subjects) met the eligibility criteria. Isocaloric fructose exchange for carbohydrate raised triglycerides and lowered total cholesterol under specific conditions without affecting LDL cholesterol or HDL cholesterol. A triglyceride-raising effect without heterogeneity was seen only in type 2 diabetes when the reference carbohydrate was starch (mean difference 0.24 [95% CI 0.05–0.44]), dose was >60 g/day (0.18 [0.00–0.37]), or follow-up was ≤4 weeks (0.18 [0.00–0.35]). Piecewise meta-regression confirmed a dose threshold of 60 g/day (R2 = 0.13)/10% energy (R2 = 0.36). A total cholesterol–lowering effect without heterogeneity was seen only in type 2 diabetes under the following conditions: no randomization and poor study quality (−0.19 [−0.34 to −0.05]), dietary fat >30% energy (−0.33 [−0.52 to −0.15]), or crystalline fructose (−0.28 [−0.47 to −0.09]). Multivariate meta-regression analyses were largely in agreement. CONCLUSIONS Pooled analyses demonstrated conditional triglyceride-raising and total cholesterol–lowering effects of isocaloric fructose exchange for carbohydrate in type 2 diabetes. Recommendations and large-scale future trials need to address the heterogeneity in the data. PMID:19592634

Edema is not a reliable diagnostic sign to exclude small brain metastases.

PubMed

Schneider, Tanja; Kuhne, Jan Felix; Bittrich, Paul; Schroeder, Julian; Magnus, Tim; Mohme, Malte; Grosser, Malte; Schoen, Gerhard; Fiehler, Jens; Siemonsen, Susanne

2017-01-01

No prior systematic study on the extent of vasogenic edema (VE) in patients with brain metastases (BM) exists. Here, we aim to determine 1) the general volumetric relationship between BM and VE, 2) a threshold diameter above which a BM shows VE, and 3) the influence of the primary tumor and location of the BM in order to improve diagnostic processes and understanding of edema formation. This single center, retrospective study includes 173 untreated patients with histologically proven BM. Semi-manual segmentation of 1416 BM on contrast-enhanced T1-weighted images and of 865 VE on fluid-attenuated inversion recovery/T2-weighted images was conducted. Statistical analyses were performed using a paired-samples t-test, linear regression/generalized mixed-effects model, and receiver-operating characteristic (ROC) curve controlling for the possible effect of non-uniformly distributed metastases among patients. For BM with non-confluent edema (n = 545), there was a statistically significant positive correlation between the volumes of the BM and the VE (P < 0.001). The optimal threshold for edema formation was a diameter of 9.4 mm for all BM. The primary tumors as interaction term in multivariate analysis had a significant influence on VE formation whereas location had not. Hence VE development is dependent on the volume of the underlying BM and the site of the primary neoplasm, but not from the location of the BM.

Keeping data continuous when analyzing the prognostic impact of a tumor marker: an example with cathepsin D in breast cancer.

PubMed

Bossard, N; Descotes, F; Bremond, A G; Bobin, Y; De Saint Hilaire, P; Golfier, F; Awada, A; Mathevet, P M; Berrerd, L; Barbier, Y; Estève, J

2003-11-01

The prognostic value of cathepsin D has been recently recognized, but as many quantitative tumor markers, its clinical use remains unclear partly because of methodological issues in defining cut-off values. Guidelines have been proposed for analyzing quantitative prognostic factors, underlining the need for keeping data continuous, instead of categorizing them. Flexible approaches, parametric and non-parametric, have been proposed in order to improve the knowledge of the functional form relating a continuous factor to the risk. We studied the prognostic value of cathepsin D in a retrospective hospital cohort of 771 patients with breast cancer, and focused our overall survival analysis, based on the Cox regression, on two flexible approaches: smoothing splines and fractional polynomials. We also determined a cut-off value from the maximum likelihood estimate of a threshold model. These different approaches complemented each other for (1) identifying the functional form relating cathepsin D to the risk, and obtaining a cut-off value and (2) optimizing the adjustment for complex covariate like age at diagnosis in the final multivariate Cox model. We found a significant increase in the death rate, reaching 70% with a doubling of the level of cathepsin D, after the threshold of 37.5 pmol mg(-1). The proper prognostic impact of this marker could be confirmed and a methodology providing appropriate ways to use markers in clinical practice was proposed.

Bowel urgency in patients with irritable bowel syndrome.

PubMed

Basilisco, Guido; De Marco, Elisabetta; Tomba, Carolina; Cesana, Bruno Mario

2007-01-01

Bowel urgency is the most bothersome symptom in irritable bowel syndrome patients with diarrhea, but its pathophysiology is poorly understood. Our aim was to assess the relationships among reporting the symptom, the reservoir functions of the colon and rectum, and the patients' psychologic profile. The study involved 28 consecutive patients with irritable bowel syndrome and 17 healthy subjects. The presence or absence of bowel urgency was verified by means of a questionnaire during the 3 days required for the ingestion of radio-opaque markers. On the fourth day, an abdominal x-ray was taken to assess colonic transit time, and rectal sensory and motor responses were measured during rectal distention. The subjects' psychologic profiles were assessed using a psychologic symptoms checklist. Forty-six percent of the patients reported urgency associated with at least 1 defecation. The multivariate logistic regression analysis showed that colonic transit was the only variable independently associated with reported bowel urgency, but the threshold for the sensation of urgency was not removed from the model since its borderline significance level. Rectal compliance was closely associated with the threshold for the sensation of urgency during rectal distention but was not an independent factor for reporting the sensation. The patients with and without urgency showed altered psychologic profiles. The symptom of urgency is associated with objective alterations in the colonic and rectal reservoir of patients with irritable bowel syndrome.

Methods to increase reproducibility in differential gene expression via meta-analysis

PubMed Central

Sweeney, Timothy E.; Haynes, Winston A.; Vallania, Francesco; Ioannidis, John P.; Khatri, Purvesh

2017-01-01

Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size. PMID:27634930

Preoperative Fasting C-Peptide Predicts Type 2 Diabetes Mellitus Remission in Low-BMI Chinese Patients After Roux-en-Y Gastric Bypass.

PubMed

Zhao, Lei; Li, Weizheng; Su, Zhihong; Liu, Yong; Zhu, Liyong; Zhu, Shaihong

2018-05-29

This study investigated the role of preoperative fasting C-peptide (FCP) levels in predicting diabetic outcomes in low-BMI Chinese patients following Roux-en-Y gastric bypass (RYGB) by comparing the metabolic outcomes of patients with FCP > 1 ng/ml versus FCP ≤ 1 ng/ml. The study sample included 78 type 2 diabetes mellitus patients with an average BMI < 30 kg/m 2 at baseline. Patients' parameters were analyzed before and after surgery, with a 2-year follow-up. A univariate logistic regression analysis and multivariate analysis of variance between the remission and improvement group were performed to determine factors that were associated with type 2 diabetes remission after RYGB. Linear correlation analyses between FCP and metabolic parameters were performed. Patients were divided into two groups: FCP > 1 ng/ml and FCP ≤ 1 ng/ml, with measured parameters compared between the groups. Patients' fasting plasma glucose, 2-h postprandial plasma glucose, FCP, and HbA1c improved significantly after surgery (p < 0.05). Factors associated with type 2 diabetes remission were BMI, 2hINS, and FCP at the univariate logistic regression analysis (p < 0.05). Multivariate logistic regression analysis was performed then showed the results were more related to FCP (OR = 2.39). FCP showed a significant linear correlation with fasting insulin and BMI (p < 0.05). There was a significant difference in remission rate between the FCP > 1 ng/ml and FCP ≤ 1 ng/ml groups (p = 0.01). The parameters of patients with FCP > 1 ng/ml, including BMI, plasma glucose, HbA1c, and plasma insulin, decreased markedly after surgery (p < 0.05). FCP level is a significant predictor of diabetes outcomes after RYGB in low-BMI Chinese patients. An FCP level of 1 ng/ml may be a useful threshold for predicting surgical prognosis, with FCP > 1 ng/ml predicting better clinical outcomes following RYGB.

Is the Critical Shields Stress for Incipient Sediment Motion Dependent on Bed Slope in Natural Channels? No.

NASA Astrophysics Data System (ADS)

Phillips, C. B.; Jerolmack, D. J.

2017-12-01

Understanding when coarse sediment begins to move in a river is essential for linking rivers to the evolution of mountainous landscapes. Unfortunately, the threshold of surface particle motion is notoriously difficult to measure in the field. However, recent studies have shown that the threshold of surface motion is empirically correlated with channel slope, a property that is easy to measure and readily available from the literature. These studies have thoroughly examined the mechanistic underpinnings behind the observed correlation and produced suitably complex models. These models are difficult to implement for natural rivers using widely available data, and thus others have treated the empirical regression between slope and the threshold of motion as a predictive model. We note that none of the authors of the original studies exploring this correlation suggested their empirical regressions be used in a predictive fashion, nevertheless these regressions between slope and the threshold of motion have found their way into numerous recent studies engendering potentially spurious conclusions. We demonstrate that there are two significant problems with using these empirical equations for prediction: (1) the empirical regressions are based on a limited sampling of the phase space of bed-load rivers and (2) the empirical measurements of bankfull and critical shear stresses are paired. The upshot of these problems limits the empirical relations predictive capacity to field sites drawn from the same region of the bed-load river phase space and that the paired nature of the data introduces a spurious correlation when considering the ratio of bankfull to critical shear stress. Using a large compilation of bed-load river hydraulic geometry data, we demonstrate that the variation within independently measured values of the threshold of motion changes systematically with bankfull shields stress and not channel slope. Additionally, we highlight using several recent datasets the potential pitfalls that one can encounter when using simplistic empirical regressions to predict the threshold of motion showing that while these concerns could be construed as subtle the resulting implications can be substantial.

Association between diabetes mellitus and hearing impairment in American and Korean populations.

PubMed

Moon, Shinje; Park, Jung Hwan; Yu, Jae Myung; Choi, Moon-Ki; Yoo, Hyung Joon

2018-04-20

The aim of this study was to evaluate ethnic- and sex-specific associations between DM and hearing impairment. For this cross-sectional study using National Health and Nutrition Examination Survey in the U.S. and Korea, the total number of eligible participants included was 7081 in the U.S. and 15,704 in Korea. Hearing impairment was defined as a pure tone threshold level ≥ 25 dB. Multivariate logistic regression analysis was conducted, adjusting for age, sex, race/ethnicity, socioeconomic status, body mass index, noise exposure, smoking, hypertension, and dyslipidemia. The association between DM and hearing impairment was found to be sex-specific. The multivariate adjusted ORs of high-frequency impairment were 0.843 (95% CI, 0.524-1.356) in American men, and 1.073 (95% CI, 0.835-1.379) in Korean men, while the ORs in women from U.S. and Korea were 1.911 (95% CI, 1.244-2.935) and 1.421 (95% CI, 1.103-1.830), respectively. A subgroup analysis of each race/ethnicity among the U.S. adults showed similar results. In contrast to high-frequency impairment, there was no significant association between low-frequency impairment and DM in both men and women. Our results suggest that DM is associated with hearing impairment in only women, irrespective of race/ethnicity groups. Copyright © 2018. Published by Elsevier Inc.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing.

PubMed

Van Hertem, T; Bahr, C; Schlageter Tello, A; Viazzi, S; Steensels, M; Romanini, C E B; Lokhorst, C; Maltz, E; Halachmi, I; Berckmans, D

2016-09-01

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

Evaluation of nonesterified fatty acids and beta-hydroxybutyrate in transition dairy cattle in the northeastern United States: Critical thresholds for prediction of clinical diseases.

PubMed

Ospina, P A; Nydam, D V; Stokol, T; Overton, T R

2010-02-01

The objectives of this study were to 1) establish cow-level critical thresholds for serum concentrations of nonesterified fatty acids (NEFA) and beta-hydroxybutyrate (BHBA) to predict periparturient diseases [displaced abomasa (DA), clinical ketosis (CK), metritis and retained placenta, or any of these three], and 2) investigate the magnitude of the metabolites' association with these diseases within 30 d in milk. In a prospective cohort study of 100 freestall, total mixed ration-fed herds in the northeastern United States, blood samples were collected from approximately 15 prepartum and 15 different postpartum transition animals in each herd, for a total of 2,758 samples. Serum NEFA concentrations were measured in the prepartum group, and both NEFA and BHBA were measured in the postpartum group. The critical thresholds for NEFA or BHBA were evaluated with receiver operator characteristic analysis for all diseases in both cohorts. The risk ratios (RR) of a disease outcome given NEFA or BHBA concentrations and other covariates were modeled with multivariable regression techniques, accounting for clustering of cows within herds. The NEFA critical threshold that predicted any of the 3 diseases in the prepartum cohort was 0.29mEq/L and in the postpartum cohort was 0.57mEq/L. The critical threshold for serum BHBA in the postpartum cohort was 10mg/dL, which predicted any of the 3 diseases. All RR with NEFA as a predictor of disease were >1.8; however, RR were greatest in animals sampled postpartum (e.g., RR for DA=9.7; 95% CI=4.2 to 22.4. All RR with BHBA as the predictor of disease were >2.3 (e.g., RR for DA=6.9; 95% CI=3.7 to 12.9). Although prepartum NEFA and postpartum BHBA were both significantly associated with development of clinical disease, postpartum serum NEFA concentration was most associated with the risk of developing DA, CK, metritis, or retained placenta during the first 30 d in milk. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Psychophysical and Patient Factors as Determinants of Pain, Function and Health Status in Shoulder Disorders

PubMed Central

Uddin, Zakir; MacDermid, Joy C.; Moro, Jaydeep; Galea, Victoria; Gross, Anita R.

2016-01-01

Objective: To estimate the extent to which psychophysical quantitative sensory test (QST) and patient factors (gender, age and comorbidity) predict pain, function and health status in people with shoulder disorders. To determine if there are gender differences for QST measures in current perception threshold (CPT), vibration threshold (VT) and pressure pain (PP) threshold and tolerance. Design: A cross-sectional study design. Setting: MacHAND Clinical Research Lab at McMaster University. Subjects: 34 surgical and 10 nonsurgical participants with shoulder pain were recruited. Method: Participants completed the following patient reported outcomes: pain (Numeric Pain Rating, Pain Catastrophizing Scale, Shoulder Pain and Disability Index) and health status (Short Form-12). Participants completed QST at 4 standardized locations and then an upper extremity performance-based endurance test (FIT-HaNSA). Pearson r’s were computed to determine the relationships between QST variables and patient factors with either pain, function or health status. Eight regression models were built to analysis QST’s and patient factors separately as predictors of either pain, function or health status. An independent sample t-test was done to evaluate the gender effect on QST. Results: Greater PP threshold and PP tolerance was significantly correlated with higher shoulder functional performance on the FIT-HANSA (r =0.31-0.44) and lower self-reported shoulder disability (r = -0.32 to -0.36). Higher comorbidity was consistently correlated (r =0.31-0.46) with more pain, and less function and health status. Older age was correlated to more pain intensity and less function (r =0.31-0.57). In multivariate models, patient factors contributed significantly to pain, function or health status models (r2 =0.19-0.36); whereas QST did not. QST was significantly different between males and females [in PP threshold (3.9 vs. 6.2, p < .001) and PP tolerance (7.6 vs. 2.6, p < .001) and CPT (1.6 vs. 2.3, p =.02)]. Conclusion: Psychophysical dimensions and patient factors (gender, age and comorbidity) affect self-reported and performance-based outcome measures in people with shoulder disorders. PMID:29399220

Associations between workplace factors and carpal tunnel syndrome: A multi-site cross sectional study.

PubMed

Fan, Z Joyce; Harris-Adamson, Carisa; Gerr, Fred; Eisen, Ellen A; Hegmann, Kurt T; Bao, Stephen; Silverstein, Barbara; Evanoff, Bradley; Dale, Ann Marie; Thiese, Matthew S; Garg, Arun; Kapellusch, Jay; Burt, Susan; Merlino, Linda; Rempel, David

2015-05-01

Few large epidemiologic studies have used rigorous case criteria, individual-level exposure measurements, and appropriate control for confounders to examine associations between workplace psychosocial and biomechanical factors and carpal tunnel syndrome (CTS). Pooling data from five independent research studies, we assessed associations between prevalent CTS and personal, work psychosocial, and biomechanical factors while adjusting for confounders using multivariable logistic regression. Prevalent CTS was associated with personal factors of older age, obesity, female sex, medical conditions, previous distal upper extremity disorders, workplace measures of peak forceful hand activity, a composite measure of force and repetition (ACGIH Threshold Limit Value for Hand Activity Level), and hand vibration. In this cross-sectional analysis of production and service workers, CTS prevalence was associated with workplace and biomechanical factors. The findings were similar to those from a prospective analysis of the same cohort with differences that may be due to recall bias and other factors. © 2015 Wiley Periodicals, Inc.

Intensely Exposed Oklahoma City Terrorism Survivors: Long-term Mental Health and Health Needs and Posttraumatic Growth.

PubMed

Tucker, Phebe; Pfefferbaum, Betty; Nitiéma, Pascal; Wendling, Tracy L; Brown, Sheryll

2016-03-01

In this study, we explore directly exposed terrorism survivors' mental health and health status, healthcare utilization, alcohol and tobacco use, and posttraumatic growth 18½ years postdisaster. Telephone surveys compared terrorism survivors and nonexposed community control subjects, using Hopkins Symptom Checklist, Breslau's PTSD screen, Posttraumatic Growth Inventory, and Health Status Questionnaire 12. Statistical analyses included multivariable logistic regression and linear modeling. Survivors, more than 80% injured, reported more anxiety and depression symptoms than did control subjects, with survivors' anxiety and depression associated with heavy drinking (≥5 drinks) and worse mental health and social functioning. While survivors had continued posttraumatic stress disorder symptoms (32 [23.2%] met probable posttraumatic stress disorder threshold), they also reported posttraumatic growth. Survivors had more care from physical, speech, respiratory, and occupational therapists. In this unprecedented long-term assessment, survivors' psychiatric symptoms, alcohol use, and ancillary health service utilization suggest unmet mental health and health needs. Extended recovery efforts might benefit from maximizing positive growth and coping.

Weather sensitivity for zoo visitation in Toronto, Canada: a quantitative analysis of historical data.

PubMed

Hewer, Micah J; Gough, William A

2016-11-01

Based on a case study of the Toronto Zoo (Canada), multivariate regression analysis, involving both climatic and social variables, was employed to assess the relationship between daily weather and visitation. Zoo visitation was most sensitive to weather variability during the shoulder season, followed by the off-season and, then, the peak season. Temperature was the most influential weather variable in relation to zoo visitation, followed by precipitation and, then, wind speed. The intensity and direction of the social and climatic variables varied between seasons. Temperatures exceeding 26 °C during the shoulder season and 28 °C during the peak season suggested a behavioural threshold associated with zoo visitation, with conditions becoming too warm for certain segments of the zoo visitor market, causing visitor numbers to decline. Even light amounts of precipitation caused average visitor numbers to decline by nearly 50 %. Increasing wind speeds also demonstrated a negative influence on zoo visitation.

Evaluation of Pressure Pain Threshold as a Measure of Perceived Stress and High Job Strain

PubMed Central

Hven, Lisbeth; Frost, Poul

2017-01-01

Objective To investigate whether pressure pain threshold (PPT), determined by pressure algometry, can be used as an objective measure of perceived stress and job strain. Methods We used cross-sectional base line data collected during 1994 to 1995 within the Project on Research and Intervention in Monotonous work (PRIM), which included 3123 employees from a variety of Danish companies. Questionnaire data included 18 items on stress symptoms, 23 items from the Karasek scale on job strain, and information on discomfort in specified anatomical regions was also collected. Clinical examinations included pressure pain algometry measurements of PPT on the trapezius and supraspinatus muscles and the tibia. Associations of stress symptoms and job strain with PPT of each site was analyzed for men and women separately with adjustment for age body mass index, and discomfort in the anatomical region closest to the point of pressure algometry using multivariable linear regression. Results We found significant inverse associations between perceived stress and PPT in both genders in models adjusting for age and body mass index: the higher level of perceived stress, the lower the threshold. For job strain, associations were weaker and only present in men. In men all associations were attenuated when adjusting for reported discomfort in regions close to the site of pressure algometry. The distributions of PPT among stressed and non-stressed persons were strongly overlapping. Conclusions Despite significant associations between perceived stress and PPT, the discriminative capability of PPT to distinguish individuals with and without stress is low. PPT measured by pressure algometry seems not applicable as a diagnostic tool of a state of mental stress. PMID:28052089

The meaning of (quality of) life in patients with eating disorders: a comparison of generic and disease-specific measures across diagnosis and outcome.

PubMed

Ackard, Diann M; Richter, Sara; Egan, Amber; Engel, Scott; Cronemeyer, Catherine L

2014-04-01

Compare general and disease-specific health-related quality of life (HRQoL) among female patients with an eating disorder (ED). Female patients (n = 221; 95.3% Caucasian; 94.0% never married) completed the Medical Outcome Short Form Health Survey (SF-36) and Eating Disorders Quality of Life (EDQoL) as part of a study of treatment outcomes. Multivariate regression models were used to compare HRQoL differences across initial ED diagnosis (85 AN-R, 19 AN-B/P, 27 BN, 90 EDNOS) and ED diagnostic classification at time of outcome assessment (140 no ED, 38 subthreshold ED, 43 full threshold ED). There were no significant differences across ED diagnosis at initial assessment on either of the SF-36 Component Summary scores. However, patients with AN-B/P scored poorer on the work/school EDQoL subscales than other ED diagnoses, and on the psychological EDQoL subscale compared to AN-R and EDNOS. At outcome assessment, comparisons across full threshold, subthreshold and no ED classification indicated that those with no ED reported better HRQoL than those with full threshold ED on the SF-36 Mental Components Summary and three of four EDQoL subscales. Furthermore, those with no ED reported better psychological HRQoL than those with subthreshold ED. Disease-specific HRQOL measures are important to use when comparing HRQoL in ED patients across treatment and outcome, and may have the sensitivity to detect meaningful differences by diagnosis more so than generic instruments. EDQoL scores from patients remitted from symptoms approach but do not reach scores for unaffected college females; thus, treatment should continue until quality of life is restored. Copyright © 2013 Wiley Periodicals, Inc.

Sensory and motor peripheral nerve function and lower-extremity quadriceps strength: the health, aging and body composition study.

PubMed

Strotmeyer, Elsa S; de Rekeneire, Nathalie; Schwartz, Ann V; Resnick, Helaine E; Goodpaster, Bret H; Faulkner, Kimberly A; Shorr, Ronald I; Vinik, Aaron I; Harris, Tamara B; Newman, Anne B

2009-11-01

To determine whether sensory and motor nerve function is associated cross-sectionally with quadriceps or ankle dorsiflexion strength in an older community-based population. Cross-sectional analyses within a longitudinal cohort study. Two U.S. clinical sites. Two thousand fifty-nine Health, Aging and Body Composition Study (Health ABC) participants (49.5% male, 36.7% black, aged 73-82) in 2000/01. Quadriceps and ankle strength were measured using an isokinetic dynamometer. Sensory and motor peripheral nerve function in the legs and feet was assessed using 10-g and 1.4-g monofilaments, vibration threshold, and peroneal motor nerve conduction amplitude and velocity. Monofilament insensitivity, poorest vibration threshold quartile (>60 mu), and poorest motor nerve conduction amplitude quartile (<1.7 mV) were associated with 11%, 7%, and 8% lower quadriceps strength (all P<.01), respectively, than in the best peripheral nerve function categories in adjusted linear regression models. Monofilament insensitivity and lowest amplitude quartile were both associated with 17% lower ankle strength (P<.01). Multivariate analyses were adjusted for demographic characteristics, diabetes mellitus, body composition, lifestyle factors, and chronic health conditions and included all peripheral nerve measures in the same model. Monofilament insensitivity (beta=-7.19), vibration threshold (beta=-0.097), and motor nerve conduction amplitude (beta=2.01) each contributed independently to lower quadriceps strength (all P<.01). Monofilament insensitivity (beta=-5.29) and amplitude (beta=1.17) each contributed independently to lower ankle strength (all P<.01). Neither diabetes mellitus status nor lean mass explained the associations between peripheral nerve function and strength. Reduced sensory and motor peripheral nerve function is related to poorer lower extremity strength in older adults, suggesting a mechanism for the relationship with lower extremity disability.

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Proposing an Empirically Justified Reference Threshold for Blood Culture Sampling Rates in Intensive Care Units

PubMed Central

Castell, Stefanie; Schwab, Frank; Geffers, Christine; Bongartz, Hannah; Brunkhorst, Frank M.; Gastmeier, Petra; Mikolajczyk, Rafael T.

2014-01-01

Early and appropriate blood culture sampling is recommended as a standard of care for patients with suspected bloodstream infections (BSI) but is rarely taken into account when quality indicators for BSI are evaluated. To date, sampling of about 100 to 200 blood culture sets per 1,000 patient-days is recommended as the target range for blood culture rates. However, the empirical basis of this recommendation is not clear. The aim of the current study was to analyze the association between blood culture rates and observed BSI rates and to derive a reference threshold for blood culture rates in intensive care units (ICUs). This study is based on data from 223 ICUs taking part in the German hospital infection surveillance system. We applied locally weighted regression and segmented Poisson regression to assess the association between blood culture rates and BSI rates. Below 80 to 90 blood culture sets per 1,000 patient-days, observed BSI rates increased with increasing blood culture rates, while there was no further increase above this threshold. Segmented Poisson regression located the threshold at 87 (95% confidence interval, 54 to 120) blood culture sets per 1,000 patient-days. Only one-third of the investigated ICUs displayed blood culture rates above this threshold. We provided empirical justification for a blood culture target threshold in ICUs. In the majority of the studied ICUs, blood culture sampling rates were below this threshold. This suggests that a substantial fraction of BSI cases might remain undetected; reporting observed BSI rates as a quality indicator without sufficiently high blood culture rates might be misleading. PMID:25520442

Understanding the trends in HIV and hepatitis C prevalence amongst injecting drug users in different settings--implications for intervention impact.

PubMed

Vickerman, Peter; Martin, Natasha K; Hickman, Matthew

2012-06-01

A recent systematic review observed that HIV prevalence amongst injectors is negligible (<1%) below a threshold HCV prevalence of 30%, but thereafter increases with HCV prevalence. We explore whether a model can reproduce these trends, what determines different epidemiological profiles and how this affects intervention impact. An HIV/HCV transmission model was developed. Univariate sensitivity analyses determined whether the model projected a HCV prevalence threshold below which HIV is negligible, and how different behavioural and epidemiological factors affect the threshold. Multivariate uncertainty analyses considered whether the model could reproduce the observed breadth of HIV/HCV epidemics, how specific behavioural patterns produce different epidemic profiles, and how this affects an intervention's impact (reduces injecting risk by 30%). The model projected a HCV prevalence threshold, which varied depending on the heterogeneity in risk, mixing, and injecting duration in a setting. Multivariate uncertainty analyses showed the model could produce the same range of observed HIV/HCV epidemics. Variability in injecting transmission risk, degree of heterogeneity and injecting duration mainly determined different epidemic profiles. The intervention resulted in 50%/28% reduction in HIV incidence/prevalence and 37%/10% reduction in HCV incidence/prevalence over five years. For either infection, greater impact occurred in settings with lower prevalence of that infection and higher prevalence of the other infection. There are threshold levels of HCV prevalence below which HIV risk is negligible but these thresholds are likely to vary by setting. A setting's HIV and HCV prevalence may give insights into IDU risk behaviour and intervention impact. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A piecewise regression approach for determining biologically relevant hydraulic thresholds for the protection of fish at river infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boys, Craig A.; Robinson, Wayne; Miller, Brett

2016-05-13

Barotrauma injury can occur when fish are exposed to rapid decompression during downstream passage through river infrastructure. A piecewise regression approach was used to objectively quantify barotrauma injury thresholds in two physoclistous species (Murray cod Maccullochella peelii and silver perch Bidyanus bidyanus) following simulated infrastructure passage in barometric chambers. The probability of injuries such as swim bladder rupture; exophthalmia; and haemorrhage and emphysema in various organs increased as the ratio between the lowest exposure pressure and the acclimation pressure (ratio of pressure change RPCE/A) fell. The relationship was typically non-linear and piecewise regression was able to quantify thresholds in RPCE/Amore » that once exceeded resulted in a substantial increase in barotrauma injury. Thresholds differed among injury types and between species but by applying a multi-species precautionary principle, the maintenance of exposure pressures at river infrastructure above 70% of acclimation pressure (RPCE/A of 0.7) should sufficiently protect downstream migrating juveniles of these two physoclistous species. These findings have important implications for determining the risk posed by current infrastructures and informing the design and operation of new ones.« less

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Mapping Shallow Landslide Slope Inestability at Large Scales Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Avalon Cullen, C.; Kashuk, S.; Temimi, M.; Suhili, R.; Khanbilvardi, R.

2015-12-01

Rainfall induced landslides are one of the most frequent hazards on slanted terrains. They lead to great economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability at large scales. Buffer and threshold techniques are used to downscale areas and minimize uncertainties. Four static parameters (slope angle, soil type, land cover and elevation) for 261 shallow rainfall-induced landslides in the continental United States are examined. ASTER GDEM is used as bases for topographical characterization of slope and buffer analysis. Slope angle threshold assessment at the 50, 75, 95, 98, and 99 percentiles is tested locally. Further analysis of each threshold in relation to other parameters is investigated in a logistic regression environment for the continental U.S. It is determined that lower than 95-percentile thresholds under-estimate slope angles. Best regression fit can be achieved when utilizing the 99-threshold slope angle. This model predicts the highest number of cases correctly at 87.0% accuracy. A one-unit rise in the 99-threshold range increases landslide likelihood by 11.8%. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional slope instability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Release from bats: genetic distance and sensoribehavioural regression in the Pacific field cricket, Teleogryllus oceanicus

NASA Astrophysics Data System (ADS)

Fullard, James H.; Ter Hofstede, Hannah M.; Ratcliffe, John M.; Pollack, Gerald S.; Brigidi, Gian S.; Tinghitella, Robin M.; Zuk, Marlene

2010-01-01

The auditory thresholds of the AN2 interneuron and the behavioural thresholds of the anti-bat flight-steering responses that this cell evokes are less sensitive in female Pacific field crickets that live where bats have never existed (Moorea) compared with individuals subjected to intense levels of bat predation (Australia). In contrast, the sensitivity of the auditory interneuron, ON1 which participates in the processing of both social signals and bat calls, and the thresholds for flight orientation to a model of the calling song of male crickets show few differences between the two populations. Genetic analyses confirm that the two populations are significantly distinct, and we conclude that the absence of bats has caused partial regression in the nervous control of a defensive behaviour in this insect. This study represents the first examination of natural evolutionary regression in the neural basis of a behaviour along a selection gradient within a single species.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Using CART to Identify Thresholds and Hierarchies in the Determinants of Funding Decisions.

PubMed

Schilling, Chris; Mortimer, Duncan; Dalziel, Kim

2017-02-01

There is much interest in understanding decision-making processes that determine funding outcomes for health interventions. We use classification and regression trees (CART) to identify cost-effectiveness thresholds and hierarchies in the determinants of funding decisions. The hierarchical structure of CART is suited to analyzing complex conditional and nonlinear relationships. Our analysis uncovered hierarchies where interventions were grouped according to their type and objective. Cost-effectiveness thresholds varied markedly depending on which group the intervention belonged to: lifestyle-type interventions with a prevention objective had an incremental cost-effectiveness threshold of $2356, suggesting that such interventions need to be close to cost saving or dominant to be funded. For lifestyle-type interventions with a treatment objective, the threshold was much higher at $37,024. Lower down the tree, intervention attributes such as the level of patient contribution and the eligibility for government reimbursement influenced the likelihood of funding within groups of similar interventions. Comparison between our CART models and previously published results demonstrated concurrence with standard regression techniques while providing additional insights regarding the role of the funding environment and the structure of decision-maker preferences.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

A Practical Guide to Regression Discontinuity

ERIC Educational Resources Information Center

Jacob, Robin; Zhu, Pei; Somers, Marie-Andrée; Bloom, Howard

2012-01-01

Regression discontinuity (RD) analysis is a rigorous nonexperimental approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cut-point. Over the last two decades, the regression discontinuity approach has…

Understanding antimicrobial stewardship: Disease severity treatment thresholds and antimicrobial alternatives among organic and conventional calf producers.

PubMed

Habing, Greg; Djordjevic, Catherine; Schuenemann, Gustavo M; Lakritz, Jeff

2016-08-01

Reductions in livestock antimicrobial use (AMU) can be achieved through identification of effective antimicrobial alternatives as well as accurate and stringent identification of cases requiring antimicrobial therapy. Objective measurements of selectivity that incorporate appropriate case definitions are necessary to understand the need and potential for reductions in AMU through judicious use. The objective of this study was to measure selectivity using a novel disease severity treatment threshold for calf diarrhea, and identify predictors of more selective application of antimicrobials among conventional dairy producers. A second objective of this study was to describe the usage frequency and perceptions of efficacy of common antimicrobial alternatives among conventional and organic producers. The cross-sectional survey was mailed to Michigan and Ohio, USA dairy producers and contained questions on AMU attitudes, AMU practices, veterinary-written protocols, and antimicrobial alternatives. The treatment threshold, defined based on the case severity where the producer would normally apply antimicrobials, was identified with a series of descriptions with increasing severity, and ordinal multivariable logistic regression was used to determine the association between the treatment threshold and individual or herd characteristics. The response rate was 49% (727/1488). Overall, 42% of conventional producers reported any veterinary-written treatment protocol, and 27% (113/412) of conventional producers had a veterinary-written protocol for the treatment of diarrhea that included a case identification. The majority (58%, 253/437) of conventional producers, but a minority (7%) of organic producers disagreed that antibiotic use in agriculture led to resistant bacterial infections in people. Among conventional producers, the proportion of producers applying antimicrobials for therapy increased from 13% to 67% with increasing case severity. The treatment threshold was low, medium, and high for 11% (47/419), 57% (251/419), and 28% (121/419) of conventional producers, respectively. Treatment threshold was not significantly associated with the use of protocols or frequency of veterinary visits; however, individuals with more concern for the public health impact of livestock AMU had a significantly higher treatment threshold (i.e. more selective) (p<0.05). Alternative therapies were used by both organic and conventional producers, but, garlic, aloe, and "other herbal therapies" with little documented efficacy were used by a majority (>60%) of organic producers. Overall, findings from this study highlight the need for research on antimicrobial alternatives, wider application of treatment protocols, and farm personnel education and training on diagnostic criteria for initiation of antimicrobial therapy. Copyright © 2016 Elsevier B.V. All rights reserved.

Threshold Velocity for Saltation Activity in the Taklimakan Desert

NASA Astrophysics Data System (ADS)

Yang, Xinghua; He, Qing; Matimin, Ali; Yang, Fan; Huo, Wen; Liu, Xinchun; Zhao, Tianliang; Shen, Shuanghe

2017-12-01

The threshold velocity is an indicator of a soil's susceptibility to saltation activity and is also an important parameter in dust emission models. In this study, the saltation activity, atmospheric conditions, and soil conditions were measured from 1 August 2008 to 31 July 2009 in the Taklimakan Desert, China. the threshold velocity was estimated using the Gaussian time fraction equivalence method. At 2 m height, the 1-min averaged threshold velocity varied between 3.5 and 10.9 m/s, with a mean of 5.9 m/s. Threshold velocities varying between 4.5 and 7.5 m/s accounted for about 91.4% of all measurements. The average threshold velocity displayed clear seasonal variations in the following sequence: winter (5.1 m/s) < autumn (5.8 m/s) < spring (6.1 m/s) < summer (6.5 m/s). A regression equation of threshold velocity was established based on the relations between daily mean threshold velocity and air temperature, specific humidity, and soil volumetric moisture content. High or moderate positive correlations were found between threshold velocity and air temperature, specific humidity, and soil volumetric moisture content (air temperature r = 0.75; specific humidity r = 0.59; and soil volumetric moisture content r = 0.55; sample size = 251). In the study area, the observed horizontal dust flux was 4198.0 kg/m during the whole period of observation, while the horizontal dust flux calculated using the threshold velocity from the regression equation was 4675.6 kg/m. The correlation coefficient between the calculated result and the observations was 0.91. These results indicate that atmospheric and soil conditions should not be neglected in parameterization schemes for threshold velocity.

High-throughput quantitative biochemical characterization of algal biomass by NIR spectroscopy; multiple linear regression and multivariate linear regression analysis.

PubMed

Laurens, L M L; Wolfrum, E J

2013-12-18

One of the challenges associated with microalgal biomass characterization and the comparison of microalgal strains and conversion processes is the rapid determination of the composition of algae. We have developed and applied a high-throughput screening technology based on near-infrared (NIR) spectroscopy for the rapid and accurate determination of algal biomass composition. We show that NIR spectroscopy can accurately predict the full composition using multivariate linear regression analysis of varying lipid, protein, and carbohydrate content of algal biomass samples from three strains. We also demonstrate a high quality of predictions of an independent validation set. A high-throughput 96-well configuration for spectroscopy gives equally good prediction relative to a ring-cup configuration, and thus, spectra can be obtained from as little as 10-20 mg of material. We found that lipids exhibit a dominant, distinct, and unique fingerprint in the NIR spectrum that allows for the use of single and multiple linear regression of respective wavelengths for the prediction of the biomass lipid content. This is not the case for carbohydrate and protein content, and thus, the use of multivariate statistical modeling approaches remains necessary.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Association of daily asthma emergency department visits and hospital admissions with ambient air pollutants among the pediatric Medicaid population in Detroit: time-series and time-stratified case-crossover analyses with threshold effects.

PubMed

Li, Shi; Batterman, Stuart; Wasilevich, Elizabeth; Wahl, Robert; Wirth, Julie; Su, Feng-Chiao; Mukherjee, Bhramar

2011-11-01

Asthma morbidity has been associated with ambient air pollutants in time-series and case-crossover studies. In such study designs, threshold effects of air pollutants on asthma outcomes have been relatively unexplored, which are of potential interest for exploring concentration-response relationships. This study analyzes daily data on the asthma morbidity experienced by the pediatric Medicaid population (ages 2-18 years) of Detroit, Michigan and concentrations of pollutants fine particles (PM2.5), CO, NO2 and SO2 for the 2004-2006 period, using both time-series and case-crossover designs. We use a simple, testable and readily implementable profile likelihood-based approach to estimate threshold parameters in both designs. Evidence of significant increases in daily acute asthma events was found for SO2 and PM2.5, and a significant threshold effect was estimated for PM2.5 at 13 and 11 μg m(-3) using generalized additive models and conditional logistic regression models, respectively. Stronger effect sizes above the threshold were typically noted compared to standard linear relationship, e.g., in the time series analysis, an interquartile range increase (9.2 μg m(-3)) in PM2.5 (5-day-moving average) had a risk ratio of 1.030 (95% CI: 1.001, 1.061) in the generalized additive models, and 1.066 (95% CI: 1.031, 1.102) in the threshold generalized additive models. The corresponding estimates for the case-crossover design were 1.039 (95% CI: 1.013, 1.066) in the conditional logistic regression, and 1.054 (95% CI: 1.023, 1.086) in the threshold conditional logistic regression. This study indicates that the associations of SO2 and PM2.5 concentrations with asthma emergency department visits and hospitalizations, as well as the estimated PM2.5 threshold were fairly consistent across time-series and case-crossover analyses, and suggests that effect estimates based on linear models (without thresholds) may underestimate the true risk. Copyright © 2011 Elsevier Inc. All rights reserved.

Refusal of Recommended Chemotherapy for Ovarian Cancer: Risk Factors and Outcomes; a National Cancer Data Base Study.

PubMed

Wallace, Sumer K; Lin, Jeff F; Cliby, William A; Leiserowitz, Gary S; Tergas, Ana I; Bristow, Robert E

2016-05-01

To identify risk factors associated with refusal of recommended chemotherapy and its impact on patients with epithelial ovarian cancer (EOC). We identified patients in the National Cancer Data Base diagnosed with EOC from January 1998 to December 2011. Patients who refused chemotherapy were identified and compared with those who received recommended, multiagent chemotherapy. Univariate and multivariable analyses were performed using chi-square test with Bonferroni correction, binary logistic regression, log-rank test, and Cox proportional hazards modeling. The threshold for statistical significance was set at a P value of less than 0.05. From a cohort of 147,713 eligible patients, 2,707 refused chemotherapy. These patients were compared with 92,212 patients who received recommended multiagent chemotherapy. Older age, more medical comorbidities, not having insurance, and later year of diagnosis were directly and significantly associated with chemotherapy refusal when analyzed using multivariable logistic regression. In addition, lower-than-expected facility adherence to NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Ovarian Cancer, treatment at low-volume center, lower grade, and higher stage were all significantly and independently associated with chemotherapy refusal. Median overall survival of patients who received multiagent chemotherapy was significantly longer than that of those who refused chemotherapy (43 vs 4.8 months; P<.0005). After controlling for known patient, facility, and disease prognostic factors, chemotherapy refusal is significantly associated with increased risk of death. Refusal of recommended chemotherapy carries significant risk of early death from ovarian cancer. Our data demonstrate that the decision to refuse chemotherapy is multifactorial and, in addition to unalterable factors (eg, stage/grade, age), involves factors that can be changed, including facility type and payor. Efforts at addressing these discrepancies in care can improve compliance with chemotherapy recommendations in the NCCN Guidelines for Ovarian Cancer and outcomes. Copyright © 2016 by the National Comprehensive Cancer Network.

[The analysis of threshold effect using Empower Stats software].

PubMed

Lin, Lin; Chen, Chang-zhong; Yu, Xiao-dan

2013-11-01

In many studies about biomedical research factors influence on the outcome variable, it has no influence or has a positive effect within a certain range. Exceeding a certain threshold value, the size of the effect and/or orientation will change, which called threshold effect. Whether there are threshold effects in the analysis of factors (x) on the outcome variable (y), it can be observed through a smooth curve fitting to see whether there is a piecewise linear relationship. And then using segmented regression model, LRT test and Bootstrap resampling method to analyze the threshold effect. Empower Stats software developed by American X & Y Solutions Inc has a threshold effect analysis module. You can input the threshold value at a given threshold segmentation simulated data. You may not input the threshold, but determined the optimal threshold analog data by the software automatically, and calculated the threshold confidence intervals.

Double-adjustment in propensity score matching analysis: choosing a threshold for considering residual imbalance.

PubMed

Nguyen, Tri-Long; Collins, Gary S; Spence, Jessica; Daurès, Jean-Pierre; Devereaux, P J; Landais, Paul; Le Manach, Yannick

2017-04-28

Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering.

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Error minimization algorithm for comparative quantitative PCR analysis: Q-Anal.

PubMed

OConnor, William; Runquist, Elizabeth A

2008-07-01

Current methods for comparative quantitative polymerase chain reaction (qPCR) analysis, the threshold and extrapolation methods, either make assumptions about PCR efficiency that require an arbitrary threshold selection process or extrapolate to estimate relative levels of messenger RNA (mRNA) transcripts. Here we describe an algorithm, Q-Anal, that blends elements from current methods to by-pass assumptions regarding PCR efficiency and improve the threshold selection process to minimize error in comparative qPCR analysis. This algorithm uses iterative linear regression to identify the exponential phase for both target and reference amplicons and then selects, by minimizing linear regression error, a fluorescence threshold where efficiencies for both amplicons have been defined. From this defined fluorescence threshold, cycle time (Ct) and the error for both amplicons are calculated and used to determine the expression ratio. Ratios in complementary DNA (cDNA) dilution assays from qPCR data were analyzed by the Q-Anal method and compared with the threshold method and an extrapolation method. Dilution ratios determined by the Q-Anal and threshold methods were 86 to 118% of the expected cDNA ratios, but relative errors for the Q-Anal method were 4 to 10% in comparison with 4 to 34% for the threshold method. In contrast, ratios determined by an extrapolation method were 32 to 242% of the expected cDNA ratios, with relative errors of 67 to 193%. Q-Anal will be a valuable and quick method for minimizing error in comparative qPCR analysis.

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

The effects of competition on premiums: using United Healthcare's 2015 entry into Affordable Care Act's marketplaces as an instrumental variable.

PubMed

Agirdas, Cagdas; Krebs, Robert J; Yano, Masato

2018-01-08

One goal of the Affordable Care Act is to increase insurance coverage by improving competition and lowering premiums. To facilitate this goal, the federal government enacted online marketplaces in the 395 rating areas spanning 34 states that chose not to establish their own state-run marketplaces. Few multivariate regression studies analyzing the effects of competition on premiums suffer from endogeneity, due to simultaneity and omitted variable biases. However, United Healthcare's decision to enter these marketplaces in 2015 provides the researcher with an opportunity to address this endogeneity problem. Exploiting the variation caused by United Healthcare's entry decision as an instrument for competition, we study the impact of competition on premiums during the first 2 years of these marketplaces. Combining panel data from five different sources and controlling for 12 variables, we find that one more insurer in a rating area leads to a 6.97% reduction in the second-lowest-priced silver plan premium, which is larger than the estimated effects in existing literature. Furthermore, we run a threshold analysis and find that competition's effects on premiums become statistically insignificant if there are four or more insurers in a rating area. These findings are robust to alternative measures of premiums, inclusion of a non-linear term in the regression models and a county-level analysis.

Estimating Information Processing in a Memory System: The Utility of Meta-analytic Methods for Genetics.

PubMed

Yildizoglu, Tugce; Weislogel, Jan-Marek; Mohammad, Farhan; Chan, Edwin S-Y; Assam, Pryseley N; Claridge-Chang, Adam

2015-12-01

Genetic studies in Drosophila reveal that olfactory memory relies on a brain structure called the mushroom body. The mainstream view is that each of the three lobes of the mushroom body play specialized roles in short-term aversive olfactory memory, but a number of studies have made divergent conclusions based on their varying experimental findings. Like many fields, neurogenetics uses null hypothesis significance testing for data analysis. Critics of significance testing claim that this method promotes discrepancies by using arbitrary thresholds (α) to apply reject/accept dichotomies to continuous data, which is not reflective of the biological reality of quantitative phenotypes. We explored using estimation statistics, an alternative data analysis framework, to examine published fly short-term memory data. Systematic review was used to identify behavioral experiments examining the physiological basis of olfactory memory and meta-analytic approaches were applied to assess the role of lobular specialization. Multivariate meta-regression models revealed that short-term memory lobular specialization is not supported by the data; it identified the cellular extent of a transgenic driver as the major predictor of its effect on short-term memory. These findings demonstrate that effect sizes, meta-analysis, meta-regression, hierarchical models and estimation methods in general can be successfully harnessed to identify knowledge gaps, synthesize divergent results, accommodate heterogeneous experimental design and quantify genetic mechanisms.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Analyses related to the development of DSM-5 criteria for substance use related disorders: 3. An assessment of Pathological Gambling criteria.

PubMed

Denis, Cécile; Fatséas, Mélina; Auriacombe, Marc

2012-04-01

The DSM-5 Substance-Related Disorders Work Group proposed to include Pathological Gambling within the current Substance-Related Disorders section. The objective of the current report was to assess four possible sets of diagnostic criteria for Pathological Gambling. Gamblers (N=161) were defined as either Pathological or Non-Pathological according to four classification methods. (a) Option 1: the current DSM-IV criteria for Pathological Gambling; (b) Option 2: dropping the "Illegal Acts" criterion, while keeping the threshold at 5 required criteria endorsed; (c) Option 3: the proposed DSM-5 approach, i.e., deleting "Illegal Acts" and lowering the threshold of required criteria from 5 to 4; (d) Option 4: to use a set of Pathological Gambling criteria modeled on the DSM-IV Substance Dependence criteria. Cronbach's alpha and eigenvalues were calculated for reliability, Phi, discriminant function analyses, correlations and multivariate regression models were performed for validity and kappa coefficients were calculated for diagnostic consistency of each option. All criteria sets were reliable and valid. Some criteria had higher discriminant properties than others. The proposed DSM-5 criteria in Options 2 and 3 performed well and did not appear to alter the meanings of the diagnoses of Pathological Gambling from DSM-IV. Future work should further explore if Pathological Gambling might be assessed using the same criteria as those used for Substance Use Disorders. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Cumulative effects of HIV illness and caring for children orphaned by AIDS on anxiety symptoms among adults caring for children in HIV-endemic South Africa.

PubMed

Kuo, Caroline; Cluver, Lucie; Casale, Marisa; Lane, Tyler

2014-06-01

Adults caring for children in HIV-endemic communities are at risk for poor psychological outcomes. However, we still have a limited understanding of how various HIV impacts--including caregiver's own HIV illness, responsibilities of caring for a child orphaned by AIDS, or both--affect psychological outcomes among caregivers. Furthermore, few studies have explored the relationship between stigma, HIV, and psychological outcomes among caregivers of children in HIV-endemic communities. A cross-sectional survey conducted from 2009 to 2010 assessed anxiety among 2477 caregivers of children in HIV-endemic South Africa. Chi-square tested differences in anxiety among caregivers living with HIV, caregivers of a child orphaned by AIDS, and caregivers affected with both conditions. Multivariate logistic regressions identified whether the relationship between HIV impacts and anxiety remained after controlling for socio-demographic co-factors. Mediation analysis tested the relationship between stigma, HIV, and anxiety. The odds of meeting threshold criteria for clinically relevant anxiety symptoms were two and a half times greater among caregivers living with HIV compared to nonaffected caregivers. The odds of meeting threshold criteria for clinically relevant anxiety symptoms were greatest among caregivers living with HIV and caring for a child orphaned by AIDS. Exposure to AIDS-related stigma partially mediated the relationship between HIV and anxiety. Interventions are needed to address caregiver psychological health, particularly among caregivers affected with both conditions of living with HIV and caring for a child orphaned by AIDS.

Forward head posture is associated with pressure pain threshold and neck pain duration in university students with subclinical neck pain.

PubMed

Pacheco, Joana; Raimundo, João; Santos, Filipe; Ferreira, Mário; Lopes, Tiago; Ramos, Luis; Silva, Anabela G

2018-06-08

The aims of this study are to investigate the association between: (i) forward head posture (FHP) and pressure pain thresholds (PPTs); (ii) FHP and maladaptive cognitive processes; and (iii) FHP and neck pain characteristics in university students with subclinical neck pain. A total of 140 university students, 90 asymptomatic and 50 with subclinical neck pain, entered the study. Demographic data, anthropometric data, FHP, and PPTs were collected for both groups. In addition, pain characteristics, pain catastrophizing, and fear of movement were assessed for participants with neck pain. FHP was characterized by the angle between C7, the tragus of the ear, and the horizontal line. Correlation analysis and multivariate regression analysis were conducted. Participants with subclinical neck pain showed significantly lower PPTs than participants without neck pain (p < .05), but similar FHP (p > .05). No significant association was found between FHP and PPTs in the asymptomatic group. In the group of participants with subclinical neck pain, PPTs at the right trapezius and neck pain duration explained 19% of the variance of FHP (R 2  = 0.23; adjusted R 2  = 0.19; p < .05). This study suggests that FHP is not associated with PPTs in asymptomatic university students. In university students with subclinical neck pain, increased FHP was associated with right trapezius hypoalgesia and with neck pain of shorter duration. These findings are in contrast with current assumptions on the association between neck pain and FHP.

Effect of peripheral arterial disease on the onset of lactate threshold during cardiopulmonary exercise test: study protocol.

PubMed

Key, Angela; Ali, Tamara; Walker, Paul; Duffy, Nick; Barkat, Mo; Snellgrove, Jayne; Torella, Francesco

2016-12-19

Cardiopulmonary exercise test (CPET) is widely used in preoperative assessment and cardiopulmonary rehabilitation. The effect of peripheral arterial disease (PAD) on oxygen delivery (VO 2 ) measured by CPET is not known. The aim of this study was to investigate the effect of PAD on VO 2 measurements during CPET. We designed a prospective cohort study, which will recruit 30 patients with PAD, who will undergo CPET before and after treatment of iliofemoral occlusive arterial disease. The main outcome measure is the difference in VO 2 at the lactate threshold (LT) between the 2 CPETs. The secondary outcome measure is the relationship between change in VO 2 at the LT and peak exercise pretreatment and post-treatment and haemodynamic measures of PAD improvement (ankle-brachial index differential). For VO 2 changes, only simple paired bivariate comparisons, not multivariate analyses, are planned, due to the small sample size. The correlation between ABI and VO 2 rise will be tested by linear regression. The study was approved by the North West-Lancaster Research and Ethics committee (reference 15/NW/0801). Results will be disseminated through scientific journal and scientific conference presentation. Completion of recruitment is expected by the end of 2016, and submission for publication by March 2017. NCT02657278. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Margin to tumor thickness ratio - A predictor of local recurrence and survival in oral squamous cell carcinoma.

PubMed

Heiduschka, Gregor; Virk, Sohaib A; Palme, Carsten E; Ch'ng, Sydney; Elliot, Michael; Gupta, Ruta; Clark, Jonathan

2016-04-01

To assess whether small oral squamous cell carcinomas (OSCC) require the same margin clearance as large tumors. We evaluated the association between the ratio of the closest margin to tumor size (MSR) and tumor thickness (MTR) with local control and survival. The clinicopathologic and follow up data were obtained for 501 OSCC patients who had surgical resection with curative intent at our institution. MTR and MSR were computed and their associations with local control and survival were assessed using multivariable Cox-regression model. Survival curves were generated using the Kaplan-Meier method. MTR was a better predictor of disease control than MSR. MTR was a predictor of local failure (p=0.033) and disease specific death (p=0.038) after adjusting for perineural invasion, lymphovascular involvement, nodal status, and radiotherapy. A threshold MTR value of 0.3 was identified, above which the risk of local recurrence was low. The ratio of margin to tumor thickness was an independent predictor for local recurrence and disease specific death in this cohort. A MTR>0.3 can serve as a useful tool for adjuvant therapy planning as it combines tumor thickness and margin clearance, two well established prognostic factors. The minimum safe margin can be calculated by multiplying the tumor thickness by 0.3. Further prospective studies in other institutions are warranted to confirm the prognostic utility of MTR and assess the generalizability of our threshold values. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biomechanical, psychosocial and individual risk factors predicting low back functional impairment among furniture distribution employees

PubMed Central

Ferguson, Sue A.; Allread, W. Gary; Burr, Deborah L.; Heaney, Catherine; Marras, William S.

2013-01-01

Background Biomechanical, psychosocial and individual risk factors for low back disorder have been studied extensively however few researchers have examined all three risk factors. The objective of this was to develop a low back disorder risk model in furniture distribution workers using biomechanical, psychosocial and individual risk factors. Methods This was a prospective study with a six month follow-up time. There were 454 subjects at 9 furniture distribution facilities enrolled in the study. Biomechanical exposure was evaluated using the American Conference of Governmental Industrial Hygienists (2001) lifting threshold limit values for low back injury risk. Psychosocial and individual risk factors were evaluated via questionnaires. Low back health functional status was measured using the lumbar motion monitor. Low back disorder cases were defined as a loss of low back functional performance of −0.14 or more. Findings There were 92 cases of meaningful loss in low back functional performance and 185 non cases. A multivariate logistic regression model included baseline functional performance probability, facility, perceived workload, intermediated reach distance number of exertions above threshold limit values, job tenure manual material handling, and age combined to provide a model sensitivity of 68.5% and specificity of 71.9%. Interpretation: The results of this study indicate which biomechanical, individual and psychosocial risk factors are important as well as how much of each risk factor is too much resulting in increased risk of low back disorder among furniture distribution workers. PMID:21955915

Prevalence and risk factors of noise-induced hearing loss among liquefied petroleum gas (LPG) cylinder infusion workers in Taiwan.

PubMed

Chang, Shu-Ju; Chang, Chin-Kuo

2009-12-01

We assessed the exposure levels of noise, estimated prevalence, and identify risk factors of noise-induced hearing loss (NIHL) among male workers with a cross-sectional study in a liquefied petroleum gas cylinder infusion factory in Taipei City. Male in-field workers exposed to noise and administrative controls were enrolled in 2006 and 2007. Face-to-face interviews were applied for demographics, employment history, and drinking/smoking habit. We then performed the measurements on noise levels in field and administration area, and hearing thresholds on study subjects with standard apparatus and protocols. Existence of hearing loss > 25 dBHL for the average of 500 Hz, 1 kHz, and 2 kHz was accordingly determined for NIHL. The effects from noise exposure, predisposing characteristics, employment-related factors, and personal habits to NIHL were estimated by univariate and multivariate logistic regressions. A total of 75 subjects were involved in research and 56.8% of in-field workers had NIHL. Between the in-field and administration groups, hearing thresholds on the worse ear showed significant differences at frequencies of 4 k, 6 k, and 8 kHz with aging considered. Adjusted odds ratio for field noise exposure (OR=99.57, 95% CI: 3.53, 2,808.74) and frequent tea or coffee consumption (OR=0.03, 95% CI: 0.01, 0.51) were found significant. Current study addressed NIHL in a specific industry in Taiwan. Further efforts in minimizing its impact are still in need.

Socializing in an open drug scene: the relationship between access to private space and drug-related street disorder.

PubMed

Debeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2012-01-01

Limited attention has been given to the potential role that the structure of housing available to people who are entrenched in street-based drug scenes may play in influencing the amount of time injection drug users (IDU) spend on public streets. We sought to examine the relationship between time spent socializing in Vancouver's drug scene and access to private space. Using multivariate logistic regression we evaluated factors associated with socializing (three+ hours each day) in Vancouver's open drug scene among a prospective cohort of IDU. We also assessed attitudes towards relocating socializing activities if greater access to private indoor space was provided. Among our sample of 1114 IDU, 43% fit our criteria for socializing in the open drug scene. In multivariate analysis, having limited access to private space was independently associated with socializing (adjusted odds ratio: 1.80, 95% confidence interval: 1.28-2.55). In further analysis, 65% of 'socializers' reported positive attitudes towards relocating socializing if they had greater access to private space. These findings suggest that providing IDU with greater access to private indoor space may reduce one component of drug-related street disorder. Low-threshold supportive housing based on the 'housing first' model that include safeguards to manage behaviors associated with illicit drug use appear to offer important opportunities to create the types of private spaces that could support a reduction in street disorder. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Prostate Health Index (PHI) Predicts High-stage Pathology in African American Men.

PubMed

Schwen, Zeyad R; Tosoian, Jeffrey J; Sokoll, Lori J; Mangold, Leslie; Humphreys, Elizabeth; Schaeffer, Edward M; Partin, Alan W; Ross, Ashley E

2016-04-01

To evaluate the association between the Prostate Health Index (PHI) and adverse pathology in a cohort of African American (AA) men undergoing radical prostatectomy. Eighty AA men with prostate-specific antigen (PSA) of 2-10 ng/mL underwent measurement of PSA, free PSA (fPSA), and p2PSA prior to radical prostatectomy. PHI was calculated as [(p2PSA/fPSA) × (PSA)(½)]. Biomarker association with pT3 disease was assessed using logistic regression, and covariates were added to a baseline multivariable model including digital rectal examination. Biomarker ability to predict pT3 disease was measured using the area under the receiver operator characteristic curve. Sixteen men (20%) demonstrated pT3 disease on final pathology. Mean age, PSA, and %fPSA were similar in men with and without pT3 disease (all P  >  .05), whereas PHI was significantly greater in men with pT3 disease (mean 57.2 vs 46.6, P  =  .04). Addition of PHI to the baseline multivariable model improved discriminative ability by 12.9% (P  =. .04) and yielded greater diagnostic accuracy than models, including other individual biomarkers. In AA men with PSA of 2-10 ng/mL, PHI was predictive of pT3 prostate cancer and may help to identify men at increased risk of adverse pathology. Additional studies are needed to substantiate these findings and identify appropriate thresholds for clinical use. Copyright © 2016 Elsevier Inc. All rights reserved.

Prognostic implications of stress hyperglycemia in acute ST elevation myocardial infarction. Prospective observational study.

PubMed

Sanjuán, Rafael; Núñez, Julio; Blasco, M Luisa; Miñana, Gema; Martínez-Maicas, Helena; Carbonell, Nieves; Palau, Patricia; Bodí, Vicente; Sanchis, Juan

2011-03-01

In patients with acute myocardial infarction, elevation of plasma glucose levels is associated with worse outcomes. The aim of this study was to evaluate the association between stress hyperglycemia and in-hospital mortality in patients with acute myocardial infarction with ST-segment elevation (STEMI). We analyzed 834 consecutive patients admitted for STEMI to the Coronary Care Unit of our center. Association between admission glucose and mortality was assessed with Cox regression analysis. Discriminative accuracy of the multivariate model was assessed by Harrell's C statistic. Eighty-nine (10.7%) patients died during hospitalization. Optimal threshold glycemia level of 140mg/dl on admission to predict mortality was obtained by ROC curves. Those who presented glucose ≥140mg/dl showed higher rates of malignant ventricular tachyarrhythmias (28% vs. 18%, P=.001), complicative bundle branch block (5% vs. 2%, P=.005), new atrioventricular block (9% vs. 5%, P=.05) and in-hospital mortality (15% vs. 5%, P<.001). Multivariate analysis showed that those with glycemia ≥140mg/dl exhibited a 2-fold increase of in-hospital mortality risk (95% CI: 1.2-3.5, P=.008) irrespective of diabetes mellitus status (P-value for interaction=0.487 and 0.653, respectively). Stress hyperglycemia on admission is a predictor of mortality and arrhythmias in patients with STEMI and could be used in the stratification of risk in these patients. Copyright © 2010 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Socializing in an Open Drug Scene: The relationship Between Access to Private Space and Drug-Related Street Disorder

PubMed Central

DeBeck, Kora; Wood, Evan; Qi, Jiezhi; Fu, Eric; McArthur, Doug; Montaner, Julio; Kerr, Thomas

2011-01-01

Background Limited attention has been given to the potential role that the structure of housing available to people who are entrenched in street-based drug scenes may play in influencing the amount of time injection drug users (IDU) spend on public streets. We sought to examine the relationship between time spent socializing in Vancouver's drug scene and access to private space. Methods Using multivariate logistic regression we evaluated factors associated with socializing (three+ hours each day) in Vancouver's open drug scene among a prospective cohort of IDU. We also assessed attitudes towards relocating socializing activities if greater access to private indoor space was provided. Results Among our sample of 1114 IDU, 43% fit our criteria for socializing in the open drug scene. In multivariate analysis, having limited access to private space was independently associated with socializing (adjusted odds ratio: 1.80, 95% confidence interval: 1.28 – 2.55). In further analysis, 65% of ‘socializers’ reported positive attitudes towards relocating socializing if they had greater access to private space. Conclusion These findings suggest that providing IDU with greater access to private indoor space may reduce one component of drug-related street disorder. Low-threshold supportive housing based on the ‘housing first’ model that include safeguards to manage behaviors associated with illicit drug use appear to offer important opportunities to create the types of private spaces that could support a reduction in street disorder. PMID:21764528

The relationship between intelligence and creativity: New support for the threshold hypothesis by means of empirical breakpoint detection

PubMed Central

Jauk, Emanuel; Benedek, Mathias; Dunst, Beate; Neubauer, Aljoscha C.

2013-01-01

The relationship between intelligence and creativity has been subject to empirical research for decades. Nevertheless, there is yet no consensus on how these constructs are related. One of the most prominent notions concerning the interplay between intelligence and creativity is the threshold hypothesis, which assumes that above-average intelligence represents a necessary condition for high-level creativity. While earlier research mostly supported the threshold hypothesis, it has come under fire in recent investigations. The threshold hypothesis is commonly investigated by splitting a sample at a given threshold (e.g., at 120 IQ points) and estimating separate correlations for lower and upper IQ ranges. However, there is no compelling reason why the threshold should be fixed at an IQ of 120, and to date, no attempts have been made to detect the threshold empirically. Therefore, this study examined the relationship between intelligence and different indicators of creative potential and of creative achievement by means of segmented regression analysis in a sample of 297 participants. Segmented regression allows for the detection of a threshold in continuous data by means of iterative computational algorithms. We found thresholds only for measures of creative potential but not for creative achievement. For the former the thresholds varied as a function of criteria: When investigating a liberal criterion of ideational originality (i.e., two original ideas), a threshold was detected at around 100 IQ points. In contrast, a threshold of 120 IQ points emerged when the criterion was more demanding (i.e., many original ideas). Moreover, an IQ of around 85 IQ points was found to form the threshold for a purely quantitative measure of creative potential (i.e., ideational fluency). These results confirm the threshold hypothesis for qualitative indicators of creative potential and may explain some of the observed discrepancies in previous research. In addition, we obtained evidence that once the intelligence threshold is met, personality factors become more predictive for creativity. On the contrary, no threshold was found for creative achievement, i.e. creative achievement benefits from higher intelligence even at fairly high levels of intellectual ability. PMID:23825884

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

NASA Astrophysics Data System (ADS)

Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

2017-12-01

Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the multivariate adaptive regression splines (MARS) technique, conclusions of this research work are exposed.

Re-assess Vector Indices Threshold as an Early Warning Tool for Predicting Dengue Epidemic in a Dengue Non-endemic Country

PubMed Central

Hsu, Pi-Shan; Chen, Chaur-Dong; Lian, Ie-Bin; Chao, Day-Yu

2015-01-01

Background Despite dengue dynamics being driven by complex interactions between human hosts, mosquito vectors and viruses that are influenced by climate factors, an operational model that will enable health authorities to anticipate the outbreak risk in a dengue non-endemic area has not been developed. The objectives of this study were to evaluate the temporal relationship between meteorological variables, entomological surveillance indices and confirmed dengue cases; and to establish the threshold for entomological surveillance indices including three mosquito larval indices [Breteau (BI), Container (CI) and House indices (HI)] and one adult index (AI) as an early warning tool for dengue epidemic. Methodology/Principal Findings Epidemiological, entomological and meteorological data were analyzed from 2005 to 2012 in Kaohsiung City, Taiwan. The successive waves of dengue outbreaks with different magnitudes were recorded in Kaohsiung City, and involved a dominant serotype during each epidemic. The annual indigenous dengue cases usually started from May to June and reached a peak in October to November. Vector data from 2005–2012 showed that the peak of the adult mosquito population was followed by a peak in the corresponding dengue activity with a lag period of 1–2 months. Therefore, we focused the analysis on the data from May to December and the high risk district, where the inspection of the immature and mature mosquitoes was carried out on a weekly basis and about 97.9% dengue cases occurred. The two-stage model was utilized here to estimate the risk and time-lag effect of annual dengue outbreaks in Taiwan. First, Poisson regression was used to select the optimal subset of variables and time-lags for predicting the number of dengue cases, and the final results of the multivariate analysis were selected based on the smallest AIC value. Next, each vector index models with selected variables were subjected to multiple logistic regression models to examine the accuracy of predicting the occurrence of dengue cases. The results suggested that Model-AI, BI, CI and HI predicted the occurrence of dengue cases with 83.8, 87.8, 88.3 and 88.4% accuracy, respectively. The predicting threshold based on individual Model-AI, BI, CI and HI was 0.97, 1.16, 1.79 and 0.997, respectively. Conclusion/Significance There was little evidence of quantifiable association among vector indices, meteorological factors and dengue transmission that could reliably be used for outbreak prediction. Our study here provided the proof-of-concept of how to search for the optimal model and determine the threshold for dengue epidemics. Since those factors used for prediction varied, depending on the ecology and herd immunity level under different geological areas, different thresholds may be developed for different countries using a similar structure of the two-stage model. PMID:26366874

Lymph node ratio predicts disease-specific survival in melanoma patients.

PubMed

Xing, Yan; Badgwell, Brian D; Ross, Merrick I; Gershenwald, Jeffrey E; Lee, Jeffrey E; Mansfield, Paul F; Lucci, Anthony; Cormier, Janice N

2009-06-01

The objectives of this analysis were to compare various measures associated with lymph node (LN) dissection and to identify threshold values associated with disease-specific survival (DSS) outcomes in patients with melanoma. Patients with lymph node-positive melanoma who underwent therapeutic LN dissection of the neck, axilla, and inguinal region were identified from the SEER database (1988-2005). We performed Cox multivariate analyses to determine the impact of the total number of LNs removed, number of negative LNs removed, and LN ratio on DSS. Multivariate cut-point analyses were conducted for each anatomic region to identify the threshold values associated with the largest improvement in DSS. The LN ratio was significantly associated with DSS for all LN regions. The LN ratio thresholds resulting in the greatest difference in 5-year DSS were .07, .13, and .18 for neck, axillary, and inguinal regions, respectively, corresponding to 15, 8, and 6 LNs removed per positive lymph node. After adjustment for other clinicopathologic factors, the hazard ratios (HRs) were .53 (95% confidence interval [CI], .40 to .71) in the neck, .52 (95% CI, .42 to .65) in the axillary, and .47 (95% CI, .36 to .61) in the inguinal regions for patients who met the LN ratio threshold. Among the prognostic factors examined, LN ratio was the best indicator of the extent of LN dissection, regardless of anatomic nodal region. These data provide evidence-based guidelines for defining adequate LN dissections in melanoma patients. (c) 2009 American Cancer Society.

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

PubMed

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Serum dehydroepiandrosterone sulphate, psychosocial factors and musculoskeletal pain in workers.

PubMed

Marinelli, A; Prodi, A; Pesel, G; Ronchese, F; Bovenzi, M; Negro, C; Larese Filon, F

2017-12-30

The serum level of dehydroepiandrosterone sulphate (DHEA-S) has been suggested as a biological marker of stress. To assess the association between serum DHEA-S, psychosocial factors and musculoskeletal (MS) pain in university workers. The study population included voluntary workers at the scientific departments of the University of Trieste (Italy) who underwent periodical health surveillance from January 2011 to June 2012. DHEA-S level was analysed in serum. The assessment tools included the General Health Questionnaire (GHQ) and a modified Nordic musculoskeletal symptoms questionnaire. The relation between DHEA-S, individual characteristics, pain perception and psychological factors was assessed by means of multivariable linear regression analysis. There were 189 study participants. The study population was characterized by high reward and low effort. Pain perception in the neck, shoulder, upper limbs, upper back and lower back was reported by 42, 32, 19, 29 and 43% of people, respectively. In multivariable regression analysis, gender, age and pain perception in the shoulder and upper limbs were significantly related to serum DHEA-S. Effort and overcommitment were related to shoulder and neck pain but not to DHEA-S. The GHQ score was associated with pain perception in different body sites and inversely to DHEA-S but significance was lost in multivariable regression analysis. DHEA-S was associated with age, gender and perception of MS pain, while effort-reward imbalance dimensions and GHQ score failed to reach the statistical significance in multivariable regression analysis. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society of Occupational Medicine. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

Quality Reporting of Multivariable Regression Models in Observational Studies: Review of a Representative Sample of Articles Published in Biomedical Journals.

PubMed

Real, Jordi; Forné, Carles; Roso-Llorach, Albert; Martínez-Sánchez, Jose M

2016-05-01

Controlling for confounders is a crucial step in analytical observational studies, and multivariable models are widely used as statistical adjustment techniques. However, the validation of the assumptions of the multivariable regression models (MRMs) should be made clear in scientific reporting. The objective of this study is to review the quality of statistical reporting of the most commonly used MRMs (logistic, linear, and Cox regression) that were applied in analytical observational studies published between 2003 and 2014 by journals indexed in MEDLINE.Review of a representative sample of articles indexed in MEDLINE (n = 428) with observational design and use of MRMs (logistic, linear, and Cox regression). We assessed the quality of reporting about: model assumptions and goodness-of-fit, interactions, sensitivity analysis, crude and adjusted effect estimate, and specification of more than 1 adjusted model.The tests of underlying assumptions or goodness-of-fit of the MRMs used were described in 26.2% (95% CI: 22.0-30.3) of the articles and 18.5% (95% CI: 14.8-22.1) reported the interaction analysis. Reporting of all items assessed was higher in articles published in journals with a higher impact factor.A low percentage of articles indexed in MEDLINE that used multivariable techniques provided information demonstrating rigorous application of the model selected as an adjustment method. Given the importance of these methods to the final results and conclusions of observational studies, greater rigor is required in reporting the use of MRMs in the scientific literature.

The repeatability of mean defect with size III and size V standard automated perimetry.

PubMed

Wall, Michael; Doyle, Carrie K; Zamba, K D; Artes, Paul; Johnson, Chris A

2013-02-15

The mean defect (MD) of the visual field is a global statistical index used to monitor overall visual field change over time. Our goal was to investigate the relationship of MD and its variability for two clinically used strategies (Swedish Interactive Threshold Algorithm [SITA] standard size III and full threshold size V) in glaucoma patients and controls. We tested one eye, at random, for 46 glaucoma patients and 28 ocularly healthy subjects with Humphrey program 24-2 SITA standard for size III and full threshold for size V each five times over a 5-week period. The standard deviation of MD was regressed against the MD for the five repeated tests, and quantile regression was used to show the relationship of variability and MD. A Wilcoxon test was used to compare the standard deviations of the two testing methods following quantile regression. Both types of regression analysis showed increasing variability with increasing visual field damage. Quantile regression showed modestly smaller MD confidence limits. There was a 15% decrease in SD with size V in glaucoma patients (P = 0.10) and a 12% decrease in ocularly healthy subjects (P = 0.08). The repeatability of size V MD appears to be slightly better than size III SITA testing. When using MD to determine visual field progression, a change of 1.5 to 4 decibels (dB) is needed to be outside the normal 95% confidence limits, depending on the size of the stimulus and the amount of visual field damage.

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Modeled summer background concentration nutrients and ...

EPA Pesticide Factsheets

We used regression models to predict background concentration of four water quality indictors: total nitrogen (N), total phosphorus (P), chloride, and total suspended solids (TSS), in the mid-continent (USA) great rivers, the Upper Mississippi, the Lower Missouri, and the Ohio. From best-model linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the land use and stressor variables were all set to zero the y-intercept. Except for total P on the Upper Mississippi River and chloride on the Ohio River, we were able to predict background concentration from significant regression models. In every model with more than one predictor variable, the model included at least one variable representing agricultural land use and one variable representing development. Predicted background concentration of total N was the same on the Upper Mississippi and Lower Missouri rivers (350 ug l-1), which was much lower than a published eutrophication threshold and percentile-based thresholds (25th percentile of concentration at all sites in the population) but was similar to a threshold derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (53 ug l-1) was also lower than published and percentile-based thresholds. Background TSS concentration was higher on the Lower Missouri (30 mg l-1) than the other ri

Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis.

PubMed

Wang, Xin; Li, Yan; Wei, Haoyun; Chen, Xia

2017-06-01

Classical least squares (CLS) regression is a popular multivariate statistical method used frequently for quantitative analysis using Fourier transform infrared (FT-IR) spectrometry. Classical least squares provides the best unbiased estimator for uncorrelated residual errors with zero mean and equal variance. However, the noise in FT-IR spectra, which accounts for a large portion of the residual errors, is heteroscedastic. Thus, if this noise with zero mean dominates in the residual errors, the weighted least squares (WLS) regression method described in this paper is a better estimator than CLS. However, if bias errors, such as the residual baseline error, are significant, WLS may perform worse than CLS. In this paper, we compare the effect of noise and bias error in using CLS and WLS in quantitative analysis. Results indicated that for wavenumbers with low absorbance, the bias error significantly affected the error, such that the performance of CLS is better than that of WLS. However, for wavenumbers with high absorbance, the noise significantly affected the error, and WLS proves to be better than CLS. Thus, we propose a selective weighted least squares (SWLS) regression that processes data with different wavenumbers using either CLS or WLS based on a selection criterion, i.e., lower or higher than an absorbance threshold. The effects of various factors on the optimal threshold value (OTV) for SWLS have been studied through numerical simulations. These studies reported that: (1) the concentration and the analyte type had minimal effect on OTV; and (2) the major factor that influences OTV is the ratio between the bias error and the standard deviation of the noise. The last part of this paper is dedicated to quantitative analysis of methane gas spectra, and methane/toluene mixtures gas spectra as measured using FT-IR spectrometry and CLS, WLS, and SWLS. The standard error of prediction (SEP), bias of prediction (bias), and the residual sum of squares of the errors (RSS) from the three quantitative analyses were compared. In methane gas analysis, SWLS yielded the lowest SEP and RSS among the three methods. In methane/toluene mixture gas analysis, a modification of the SWLS has been presented to tackle the bias error from other components. The SWLS without modification presents the lowest SEP in all cases but not bias and RSS. The modification of SWLS reduced the bias, which showed a lower RSS than CLS, especially for small components.

Establishment and validation of the scoring system for preoperative prediction of central lymph node metastasis in papillary thyroid carcinoma.

PubMed

Liu, Wen; Cheng, Ruochuan; Ma, Yunhai; Wang, Dan; Su, Yanjun; Diao, Chang; Zhang, Jianming; Qian, Jun; Liu, Jin

2018-05-03

Early preoperative diagnosis of central lymph node metastasis (CNM) is crucial to improve survival rates among patients with papillary thyroid carcinoma (PTC). Here, we analyzed clinical data from 2862 PTC patients and developed a scoring system using multivariable logistic regression and testified by the validation group. The predictive diagnostic effectiveness of the scoring system was evaluated based on consistency, discrimination ability, and accuracy. The scoring system considered seven variables: gender, age, tumor size, microcalcification, resistance index >0.7, multiple nodular lesions, and extrathyroid extension. The area under the receiver operating characteristic curve (AUC) was 0.742, indicating a good discrimination. Using 5 points as a diagnostic threshold, the validation results for validation group had an AUC of 0.758, indicating good discrimination and consistency in the scoring system. The sensitivity of this predictive model for preoperative diagnosis of CNM was 4 times higher than a direct ultrasound diagnosis. These data indicate that the CNM prediction model would improve preoperative diagnostic sensitivity for CNM in patients with papillary thyroid carcinoma.

Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

NASA Astrophysics Data System (ADS)

Zhu, Zhe

2017-08-01

The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

Standardized screening for periodontitis as an integral part of multidisciplinary management of adults with type 2 diabetes: an observational cross-sectional study of cohorts in the USA and UK

PubMed Central

Pumerantz, Andrew S; Bissett, Susan M; Dong, Fanglong; Ochoa, Cesar; Wassall, Rebecca R; Davila, Heidi; Barbee, Melanie; Nguyen, John; Vila, Pamela; Preshaw, Philip M

2017-01-01

Objective To determine prevalence and factors predictive of periodontitis by using a standardized assessment model in adults with type 2 diabetes. Research design and methods We performed an observational cross-sectional study to determine the burden of periodontitis in adults with type 2 diabetes attending urban, ambulatory referral centers in the USA and UK. Full-mouth probing was performed and periodontitis was diagnosed based on either a low (≥5 mm at ≥1 site) or high pocket probing-depth threshold (≥6 mm at ≥1 site). Results were stratified into a five-stage schema and integrated with other clinical variables into the novel Diabetes Cross-Disciplinary Index to function as a balanced health scorecard. Corresponding demographic and routinely collected health data were obtained and comparisons were made between patients with and without periodontitis. Multivariable logistic regression was performed to identify factors predictive of the presence or absence of periodontitis. Results Between our two cohorts, 253 patients were screened. Caucasians comprised >90% and Hispanic Americans >75% of the UK and US cohorts, respectively. Males and females were equally distributed; mean age was 53.6±11 years; and 17 (6.7%) were edentulous. Of the 236 dentate patients, 128 (54.2%) had periodontitis by low threshold and 57 (24.2%) by high threshold. Just 17 (7.2%) were periodontally healthy. No significant differences in age, HbA1c, blood pressure, body mass index, low-density lipoprotein cholesterol, or smoking status (all p>0.05) were identified between those with or without periodontitis (regardless of threshold) and none was found to be a significant predictor of disease. Conclusions Periodontitis is frequent in adults with type 2 diabetes and all should be screened. Periodontal health status can be visualized with other comorbidities and complications using a novel balanced scorecard that could facilitate patient–clinician communication, shared decision-making, and prioritization of individual healthcare needs. PMID:28761663

Sanitation and water supply coverage thresholds associated with active trachoma: Modeling cross-sectional data from 13 countries

PubMed Central

Boisson, Sophie; Willis, Rebecca; Bakhtiari, Ana; al-Khatib, Tawfik; Amer, Khaled; Batcho, Wilfrid; Courtright, Paul; Dejene, Michael; Goepogui, Andre; Kalua, Khumbo; Kebede, Biruck; Macleod, Colin K.; Madeleine, Kouakou IIunga Marie; Mbofana, Mariamo Saide Abdala; Mpyet, Caleb; Ndjemba, Jean; Olobio, Nicholas; Pavluck, Alexandre L.; Sokana, Oliver; Southisombath, Khamphoua; Taleo, Fasihah

2018-01-01

Background Facial cleanliness and sanitation are postulated to reduce trachoma transmission, but there are no previous data on community-level herd protection thresholds. We characterize associations between active trachoma, access to improved sanitation facilities, and access to improved water sources for the purpose of face washing, with the aim of estimating community-level or herd protection thresholds. Methods and findings We used cluster-sampled Global Trachoma Mapping Project data on 884,850 children aged 1–9 years from 354,990 households in 13 countries. We employed multivariable mixed-effects modified Poisson regression models to assess the relationships between water and sanitation coverage and trachomatous inflammation—follicular (TF). We observed lower TF prevalence among those with household-level access to improved sanitation (prevalence ratio, PR = 0.87; 95%CI: 0.83–0.91), and household-level access to an improved washing water source in the residence/yard (PR = 0.81; 95%CI: 0.75–0.88). Controlling for household-level water and latrine access, we found evidence of community-level protection against TF for children living in communities with high sanitation coverage (PR80–90% = 0.87; 95%CI: 0.73–1.02; PR90–100% = 0.76; 95%CI: 0.67–0.85). Community sanitation coverage levels greater than 80% were associated with herd protection against TF (PR = 0.77; 95%CI: 0.62–0.97)—that is, lower TF in individuals whose households lacked individual sanitation but who lived in communities with high sanitation coverage. For community-level water coverage, there was no apparent threshold, although we observed lower TF among several of the higher deciles of community-level water coverage. Conclusions Our study provides insights into the community water and sanitation coverage levels that might be required to best control trachoma. Our results suggest access to adequate water and sanitation can be important components in working towards the 2020 target of eliminating trachoma as a public health problem. PMID:29357365

The Risk of Cataract among Survivors of Childhood and Adolescent Cancer: A Report from the Childhood Cancer Survivor Study

PubMed Central

Chodick, Gabriel; Sigurdson, Alice J.; Kleinerman, Ruth A.; Sklar, Charles A.; Leisenring, Wendy; Mertens, Ann C.; Stovall, Marilyn; Smith, Susan A.; Weathers, Rita E.; Veiga, Lene H. S.; Robison, Leslie L.; Inskip, Peter D.

2016-01-01

With therapeutic successes and improved survival after a cancer diagnosis in childhood, increasing numbers of cancer survivors are at risk of subsequent treatment-related morbidities, including cataracts. While it is well known that the lens of the eye is one of the most radiosensitive tissues in the human body, the risks associated with radiation doses less than 2 Gy are less understood, as are the long- and short-term cataract risks from exposure to ionizing radiation at a young age. In this study, we followed 13,902 five-year survivors of childhood cancer in the Childhood Cancer Survivor Study cohort an average of 21.4 years from the date of first cancer diagnosis. For patients receiving radiotherapy, lens dose (mean: 2.2 Gy; range: 0–66 Gy) was estimated based on radiotherapy records. We used unconditional multivariable logistic regression models to evaluate prevalence of self-reported cataract in relationship to cumulative radiation dose both at five years after the initial cancer diagnosis and at the end of follow-up. We modeled the radiation effect in terms of the excess odds ratio (EOR) per Gy. We also analyzed cataract incidence starting from five years after initial cancer diagnosis to the end of follow-up using Cox regression. A total of 483 (3.5%) cataract cases were identified, including 200 (1.4%) diagnosed during the first five years of follow-up. In a multivariable logistic regression model, cataract prevalence at the end of follow-up was positively associated with lens dose in a manner consistent with a linear dose-response relationship (EOR per Gy = 0.92; 95% CI: 0.65–1.20). The odds ratio for doses between 0.5 and 1.5 Gy was elevated significantly relative to doses <0.5 Gy (OR = 2.2; 95% CI: 1.3–3.7). The results from this study indicate a strong association between ocular exposure to ionizing radiation and long-term risk of pre-senile cataract. The risk of cataract increased with increasing exposure, beginning at lens doses as low as 0.5 Gy. Our findings are in agreement with a growing body of evidence of an elevated risk for lens opacities in populations exposed to doses of ionizing radiation below the previously suggested threshold level of 2 Gy. PMID:27023263

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Predicting volumes in four Hawaii hardwoods...first multivariate equations developed

Treesearch

David A. Sharpnack

1966-01-01

Multivariate regression equations were developed for predicting board-foot (Int. 1/ 4-inch log rule ) and cubic-foot volumes in each 8.15-foot section of trees of four Hawaii hardwood species. The species are koa (Acacia koa), ohia (Metrosideros polymorpha), robusta eucalyptus (Eucalyptus robusta), and...

A Multivariate Test of the Bott Hypothesis in an Urban Irish Setting

ERIC Educational Resources Information Center

Gordon, Michael; Downing, Helen

1978-01-01

Using a sample of 686 married Irish women in Cork City the Bott hypothesis was tested, and the results of a multivariate regression analysis revealed that neither network connectedness nor the strength of the respondent's emotional ties to the network had any explanatory power. (Author)

Percolation bounds for decoding thresholds with correlated erasures in quantum LDPC codes

NASA Astrophysics Data System (ADS)

Hamilton, Kathleen; Pryadko, Leonid

Correlations between errors can dramatically affect decoding thresholds, in some cases eliminating the threshold altogether. We analyze the existence of a threshold for quantum low-density parity-check (LDPC) codes in the case of correlated erasures. When erasures are positively correlated, the corresponding multi-variate Bernoulli distribution can be modeled in terms of cluster errors, where qubits in clusters of various size can be marked all at once. In a code family with distance scaling as a power law of the code length, erasures can be always corrected below percolation on a qubit adjacency graph associated with the code. We bound this correlated percolation transition by weighted (uncorrelated) percolation on a specially constructed cluster connectivity graph, and apply our recent results to construct several bounds for the latter. This research was supported in part by the NSF Grant PHY-1416578 and by the ARO Grant W911NF-14-1-0272.

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic.

PubMed

Hopke, P K; Liu, C; Rubin, D B

2001-03-01

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.

Preoperative Thromboelastometry as a Predictor of Transfusion Requirements during Adult Living Donor Liver Transplantation.

PubMed

Fayed, Nirmeen; Mourad, Wessam; Yassen, Khaled; Görlinger, Klaus

2015-03-01

The ability to predict transfusion requirements may improve perioperative bleeding management as an integral part of a patient blood management program. Therefore, the aim of our study was to evaluate preoperative thromboelastometry as a predictor of transfusion requirements for adult living donor liver transplant recipients. The correlation between preoperative thromboelastometry variables in 100 adult living donor liver transplant recipients and intraoperative blood transfusion requirements was examined by univariate and multivariate linear regression analysis. Thresholds of thromboelastometric parameters for prediction of packed red blood cells (PRBCs), fresh frozen plasma (FFP), platelets, and cryoprecipitate transfusion requirements were determined with receiver operating characteristics analysis. The attending anesthetists were blinded to the preoperative thromboelastometric analysis. However, a thromboelastometry-guided transfusion algorithm with predefined trigger values was used intraoperatively. The transfusion triggers in this algorithm did not change during the study period. Univariate analysis confirmed significant correlations between PRBCs, FFP, platelets or cryoprecipitate transfusion requirements and most thromboelastometric variables. Backward stepwise logistic regression indicated that EXTEM coagulation time (CT), maximum clot firmness (MCF) and INTEM CT, clot formation time (CFT) and MCF are independent predictors for PRBC transfusion. EXTEM CT, CFT and FIBTEM MCF are independent predictors for FFP transfusion. Only EXTEM and INTEM MCF were independent predictors of platelet transfusion. EXTEM CFT and MCF, INTEM CT, CFT and MCF as well as FIBTEM MCF are independent predictors for cryoprecipitate transfusion. Thromboelastometry-based regression equation accounted for 63% of PRBC, 83% of FFP, 61% of cryoprecipitate, and 44% of platelet transfusion requirements. Preoperative thromboelastometric analysis is helpful to predict transfusion requirements in adult living donor liver transplant recipients. This may allow for better preparation and less cross-matching prior to surgery. The findings of our study need to be re-validated in a second prospective patient population.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

Asymmetries and Visual Field Summaries as Predictors of Glaucoma in the Ocular Hypertension Treatment Study

PubMed Central

Levine, Richard A.; Demirel, Shaban; Fan, Juanjuan; Keltner, John L.; Johnson, Chris A.; Kass, Michael A.

2007-01-01

Purpose To evaluate whether baseline visual field data and asymmetries between eyes predict the onset of primary open-angle glaucoma (POAG) in Ocular Hypertension Treatment Study (OHTS) participants. Methods A new index, mean prognosis (MP), was designed for optimal combination of visual field thresholds, to discriminate between eyes that developed POAG from eyes that did not. Baseline intraocular pressure (IOP) in fellow eyes was used to construct measures of IOP asymmetry. Age-adjusted baseline thresholds were used to develop indicators of visual field asymmetry and summary measures of visual field defects. Marginal multivariate failure time models were constructed that relate the new index MP, IOP asymmetry, and visual field asymmetry to POAG onset for OHTS participants. Results The marginal multivariate failure time analysis showed that the MP index is significantly related to POAG onset (P < 0.0001) and appears to be a more highly significant predictor of POAG onset than either mean deviation (MD; P = 0.17) or pattern standard deviation (PSD; P = 0.046). A 1-mm Hg increase in IOP asymmetry between fellow eyes is associated with a 17% increase in risk for development of POAG. When threshold asymmetry between eyes existed, the eye with lower thresholds was at a 37% greater risk of development of POAG, and this feature was more predictive of POAG onset than the visual field index MD, though not as strong a predictor as PSD. Conclusions The MP index, IOP asymmetry, and binocular test point asymmetry can assist in clinical evaluation of eyes at risk of development of POAG. PMID:16936102

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

Respiration-Averaged CT for Attenuation Correction of PET Images – Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients

PubMed Central

Cheng, Nai-Ming; Fang, Yu-Hua Dean; Tsan, Din-Li

2016-01-01

Purpose We compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters. Materials and Methods A total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation. Results SUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold. Conclusions Our results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods. PMID:26930211

Cost-Effectiveness of Antibody-Based Induction Therapy in Deceased Donor Kidney Transplantation in the United States.

PubMed

Gharibi, Zahra; Ayvaci, Mehmet U S; Hahsler, Michael; Giacoma, Tracy; Gaston, Robert S; Tanriover, Bekir

2017-06-01

Induction therapy in deceased donor kidney transplantation is costly, with wide discrepancy in utilization and a limited evidence base, particularly regarding cost-effectiveness. We linked the United States Renal Data System data set to Medicare claims to estimate cumulative costs, graft survival, and incremental cost-effectiveness ratio (ICER - cost per additional year of graft survival) within 3 years of transplantation in 19 450 deceased donor kidney transplantation recipients with Medicare as primary payer from 2000 to 2008. We divided the study cohort into high-risk (age > 60 years, panel-reactive antibody > 20%, African American race, Kidney Donor Profile Index > 50%, cold ischemia time > 24 hours) and low-risk (not having any risk factors, comprising approximately 15% of the cohort). After the elimination of dominated options, we estimated expected ICER among induction categories: no-induction, alemtuzumab, rabbit antithymocyte globulin (r-ATG), and interleukin-2 receptor-antagonist. No-induction was the least effective and most costly option in both risk groups. Depletional antibodies (r-ATG and alemtuzumab) were more cost-effective across all willingness-to-pay thresholds in the low-risk group. For the high-risk group and its subcategories, the ICER was very sensitive to the graft survival; overall both depletional antibodies were more cost-effective, mainly for higher willingness to pay threshold (US $100 000 and US $150 000). Rabbit ATG appears to achieve excellent cost-effectiveness acceptability curves (80% of the recipients) in both risk groups at US $50 000 threshold (except age > 60 years). In addition, only r-ATG was associated with graft survival benefit over no-induction category (hazard ratio, 0.91; 95% confidence interval, 0.84-0.99) in a multivariable Cox regression analysis. Antibody-based induction appears to offer substantial advantages in both cost and outcome compared with no-induction. Overall, depletional induction (preferably r-ATG) appears to offer the greatest benefits.

Is There a Minimum Number of Thyroidectomies a Surgeon Should Perform to Optimize Patient Outcomes?

PubMed

Adam, Mohamed Abdelgadir; Thomas, Samantha; Youngwirth, Linda; Hyslop, Terry; Reed, Shelby D; Scheri, Randall P; Roman, Sanziana A; Sosa, Julie A

2017-02-01

To determine the number of total thyroidectomies per surgeon per year associated with the lowest risk of complications. The surgeon volume-outcome association has been established for thyroidectomy; however, a threshold number of cases defining a "high-volume" surgeon remains unclear. Adults undergoing total thyroidectomy were identified from the Health Care Utilization Project-National Inpatient Sample (1998-2009). Multivariate logistic regression with restricted cubic splines was utilized to examine the association between the number of annual total thyroidectomies per surgeon and risk of complications. Among 16,954 patients undergoing total thyroidectomy, 47% had thyroid cancer and 53% benign disease. Median annual surgeon volume was 7 cases; 51% of surgeons performed 1 case/y. Overall, 6% of the patients experienced complications. After adjustment, the likelihood of experiencing a complication decreased with increasing surgeon volume up to 26 cases/y (P < 0.01). Among all patients, 81% had surgery by low-volume surgeons (≤25 cases/y). With adjustment, patients undergoing surgery by low-volume surgeons were more likely to experience complications (odds ratio 1.51, P = 0.002) and longer hospital stays (+12%, P = 0.006). Patients had an 87% increase in the odds of having a complication if the surgeon performed 1 case/y, 68% for 2 to 5 cases/y, 42% for 6 to 10 cases/y, 22% for 11 to 15 cases/y, 10% for 16 to 20 cases/y, and 3% for 21 to 25 cases/y. This is the first study to identify a surgeon volume threshold (>25 total thyroidectomies/y) that is associated with improved patient outcomes. Identifying a threshold number of cases defining a high-volume thyroid surgeon is important, as it has implications for quality improvement, criteria for referral and reimbursement, and surgical education.

Effect of postprandial thermogenesis on the cutaneous vasodilatory response during exercise.

PubMed

Hayashi, Keiji; Ito, Nozomi; Ichikawa, Yoko; Suzuki, Yuichi

2014-08-01

To examine the effect of postprandial thermogenesis on the cutaneous vasodilatory response, 10 healthy male subjects exercised for 30 min on a cycle ergometer at 50% of peak oxygen uptake, with and without food intake. Mean skin temperature, mean body temperature (Tb), heart rate, oxygen uptake, carbon dioxide elimination, and respiratory quotient were all significantly higher at baseline in the session with food intake than in the session without food intake. To evaluate the cutaneous vasodilatory response, relative laser Doppler flowmetry values were plotted against esophageal temperature (Tes) and Tb. Regression analysis revealed that the [Formula: see text] threshold for cutaneous vasodilation tended to be higher with food intake than without it, but there were no significant differences in the sensitivity. To clarify the effect of postprandial thermogenesis on the threshold for cutaneous vasodilation, the between-session difference in the Tes threshold and the Tb threshold were plotted against the between-session difference in baseline Tes and baseline Tb, respectively. Linear regression analysis of the resultant plot showed significant positive linear relationships (Tes: r = 0.85, P < 0.01; Tb: r = 0.67, P < 0.05). These results suggest that postprandial thermogenesis increases baseline body temperature, which raises the body temperature threshold for cutaneous vasodilation during exercise.

Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model

PubMed Central

Rehkopf, David; Tuljapurkar, Shripad; Horvitz, Carol C.

2018-01-01

Recent studies have examined the risk of poverty throughout the life course, but few have considered how transitioning in and out of poverty shape the dynamic heterogeneity and mortality disparities of a cohort at each age. Here we use state-by-age modeling to capture individual heterogeneity in crossing one of three different poverty thresholds (defined as 1×, 2× or 3× the “official” poverty threshold) at each age. We examine age-specific state structure, the remaining life expectancy, its variance, and cohort simulations for those above and below each threshold. Survival and transitioning probabilities are statistically estimated by regression analyses of data from the Health and Retirement Survey RAND data-set, and the National Longitudinal Survey of Youth. Using the results of these regression analyses, we parameterize discrete state, discrete age matrix models. We found that individuals above all three thresholds have higher annual survival than those in poverty, especially for mid-ages to about age 80. The advantage is greatest when we classify individuals based on 1× the “official” poverty threshold. The greatest discrepancy in average remaining life expectancy and its variance between those above and in poverty occurs at mid-ages for all three thresholds. And fewer individuals are in poverty between ages 40-60 for all three thresholds. Our findings are consistent with results based on other data sets, but also suggest that dynamic heterogeneity in poverty and the transience of the poverty state is associated with income-related mortality disparities (less transience, especially of those above poverty, more disparities). This paper applies the approach of age-by-stage matrix models to human demography and individual poverty dynamics. In so doing we extend the literature on individual poverty dynamics across the life course. PMID:29768416

Audiometric analyses confirm a cochlear component, disproportional to age, in stapedial otosclerosis.

PubMed

Topsakal, Vedat; Fransen, Erik; Schmerber, Sébastien; Declau, Frank; Yung, Matthew; Gordts, Frans; Van Camp, Guy; Van de Heyning, Paul

2006-09-01

To report the preoperative audiometric profile of surgically confirmed otosclerosis. Retrospective, multicenter study. Four tertiary referral centers. One thousand sixty-four surgically confirmed patients with otosclerosis. Therapeutic ear surgery for hearing improvement. Preoperative audiometric air conduction (AC) and bone conduction (BC) hearing thresholds were obtained retrospectively for 1064 patients with otosclerosis. A cross-sectional multiple linear regression analysis was performed on audiometric data of affected ears. Influences of age and sex were analyzed and age-related typical audiograms were created. Bone conduction thresholds were corrected for Carhart effect and presbyacusis; in addition, we tested to see if separate cochlear otosclerosis component existed. Corrected thresholds were than analyzed separately for progression of cochlear otosclerosis. The study population consisted of 35% men and 65% women (mean age, 44 yr). The mean pure-tone average at 0.5, 1, and 2 kHz was 57 dB hearing level. Multiple linear regression analysis showed significant progression for all measured AC and BC thresholds. The average annual threshold deterioration for AC was 0.45 dB/yr and the annual threshold deterioration for BC was 0.37 dB/yr. The average annual gap expansion was 0.08 dB/year. The corrected BC thresholds for Carhart effect and presbyacusis remained significantly different from zero, but only showed progression at 2 kHz. The preoperative audiological profile of otosclerosis is described. There is a significant sensorineural component in patients with otosclerosis planned for stapedotomy, which is worse than age-related hearing loss by itself. Deterioration rates of AC and BC thresholds have been reported, which can be helpful in clinical practice and might also guide the characterization of allegedly different phenotypes for familial and sporadic otosclerosis.

Examining the impacts of increased corn production on groundwater quality using a coupled modeling system.

PubMed

Garcia, Valerie; Cooter, Ellen; Crooks, James; Hinckley, Brian; Murphy, Mark; Xing, Xiangnan

2017-05-15

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥5mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additionally, the average number of animal feeding operations (AFOs) for these areas was nearly 5 times higher, and the mean N-fertilizer rate was 4 times higher. Finally, we found that areas prone to high groundwater nitrate-N concentrations attributable to the expansion scenario did not occur in new grid cells of irrigated grain-corn croplands, but were clustered around areas of existing corn crops. This application demonstrates the value of the coupled modeling system in developing spatially refined multi-variable models to provide information for geographic locations lacking complete observational data; and in projecting possible groundwater nitrate-N concentration outcomes under alternative future crop production scenarios. Published by Elsevier B.V.

Biomarkers of cardiovascular stress and incident chronic kidney disease.

PubMed

Ho, Jennifer E; Hwang, Shih-Jen; Wollert, Kai C; Larson, Martin G; Cheng, Susan; Kempf, Tibor; Vasan, Ramachandran S; Januzzi, James L; Wang, Thomas J; Fox, Caroline S

2013-11-01

Growth differentiation factor-15 (GDF-15), soluble ST2 (sST2), and high-sensitivity troponin I (hsTnI) are emerging predictors of adverse clinical outcomes. We examined whether circulating concentrations are related to the development of kidney disease in the community. Plasma GDF-15, sST2, and hsTnI concentrations were measured in 2614 Framingham Offspring cohort participants (mean age 57 years, 54% women) at the sixth examination cycle (1995-1998). Associations of biomarkers with incident chronic kidney disease [CKD, eGFR <60 mL · min(-1) · (1.73 m(2)) (-1), n = 276], microalbuminuria (urinary albumin to creatinine ratio ≥25 mg/g in women and 17 mg/g in men, n = 191), and rapid decline in renal function [decline in eGFR ≥3 mL · min(-1) · (1.73 m(2)) (-1) per year, n = 237], were evaluated using multivariable logistic regression; P < 0.006 was considered statistically significant in primary analyses. Participants were followed over a mean of 9.5 years. Higher plasma GDF-15 was associated with incident CKD [multivariable-adjusted odds ratio (OR) 1.9 per 1-U increase in log-GDF-15, 95% CI 1.6-2.3, P < 0.0001] and rapid decline in renal function (OR, 1.6; 95% CI, 1.3-1.8; P < 0.0001). GDF-15, sST2, and hsTnI had suggestive associations with incident microalbuminuria but did not meet the prespecified P-value threshold after multivariable adjustment. Adding plasma GDF-15 to clinical covariates improved risk prediction of incident CKD: the c-statistic increased from 0.826 to 0.845 (P = 0.0007), and categorical net reclassification was 6.3% (95% CI, 2.7-9.9%). Higher circulating GDF-15 is associated with incident renal outcomes and improves risk prediction of incident CKD. These findings may provide insights into the mechanisms of renal injury.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Donor Hemodynamics as a Predictor of Outcomes After Kidney Transplantation From Donors After Cardiac Death.

PubMed

Allen, M B; Billig, E; Reese, P P; Shults, J; Hasz, R; West, S; Abt, P L

2016-01-01

Donation after cardiac death is an important source of transplantable organs, but evidence suggests donor warm ischemia contributes to inferior outcomes. Attempts to predict recipient outcome using donor hemodynamic measurements have not yielded statistically significant results. We evaluated novel measures of donor hemodynamics as predictors of delayed graft function and graft failure in a cohort of 1050 kidneys from 566 donors. Hemodynamics were described using regression line slopes, areas under the curve, and time beyond thresholds for systolic blood pressure, oxygen saturation, and shock index (heart rate divided by systolic blood pressure). A logistic generalized estimation equation model showed that area under the curve for systolic blood pressure was predictive of delayed graft function (above median: odds ratio 1.42, 95% confidence interval [CI] 1.06-1.90). Multivariable Cox regression demonstrated that slope of oxygen saturation during the first 10 minutes after extubation was associated with graft failure (below median: hazard ratio 1.30, 95% CI 1.03-1.64), with 5-year graft survival of 70.0% (95%CI 64.5%-74.8%) for donors above the median versus 61.4% (95%CI 55.5%-66.7%) for those below the median. Among older donors, increased shock index slope was associated with increased hazard of graft failure. Validation of these findings is necessary to determine the utility of characterizing donor warm ischemia to predict recipient outcome. © Copyright 2015 The American Society of Transplantation and the American Society of Transplant Surgeons.

Circulating anti-mullerian hormone as predictor of ovarian response to clomiphene citrate in women with polycystic ovary syndrome.

PubMed

Xi, Wenyan; Yang, Yongkang; Mao, Hui; Zhao, Xiuhua; Liu, Ming; Fu, Shengyu

2016-02-11

To investigate the impact of high circulating AMH on the outcome of CC ovulation induction in women with PCOS. This prospective cohort observational study included 81 anovulatory women with PCOS who underwent 213 cycles of CC ovarian stimulation. Serum AMH concentrations were measured on cycle day 3 before the commencement of CC in the first cycle, which were compared between responders and CC-resistant anovulation (CRA). Logistic regression analysis was applied to study the value of serum AMH for the prediction of ovarian responsiveness to CC stimulation. The receiver-operating characteristic (ROC) curve was used to evaluate the prognostic value of circulating AMH. Serum AMH levels. Women who ovulated after CC therapy had a significantly lower AMH compared with the CRA (5.34 ± 1.97 vs.7.81 ± 3.49, P < 0.001). There was a significant gradient increase of serum AMH levels with the increasing dose of CC required to achieve ovulation (P < 0.05). In multivariate logistic regression analysis, AMH was an independent predictor of ovulation induction by CC in PCOS patients. ROC curve analysis showed AMH to be a useful predictor of ovulation induction by CC in PCOS patients, having 92 % specificity and 65 % sensitivity when the threshold AMH concentration was 7.77 ng/ml. Serum AMH may be clinically useful to predict which PCOS women are more likely to respond to CC treatment and thus to direct the selection of protocols of ovulation induction.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

Education as protector against dementia, but what exactly do we mean by education?

PubMed

Then, Francisca S; Luck, Tobias; Angermeyer, Matthias C; Riedel-Heller, Steffi G

2016-07-01

even though a great number of research studies have shown that high education has protective effects against dementia, some studies did not observe such a significant effect. In that respect, the aim of our study was to investigate and compare various operationalisation approaches of education and how they impact dementia risk within one sample. data were derived from the Leipzig longitudinal study of the aged (LEILA75+). Individuals aged 75 and older underwent six cognitive assessments at an interval of 1.5 years and a final follow-up 15 years after the baseline assessment. We operationalised education according to different approaches used in previous studies and analysed the impact on dementia incidence via multivariate cox regression modelling. the results showed that whether education is identified as significant protector against dementia strongly depends on the operationalisation of education. Whereas the pure number of years of education showed statistically significant protective effects on dementia risk, other more complex categorical classification approaches did not. Moreover, completing >10 years of education or a tertiary level seems to be an important threshold to significantly reduce dementia risk. findings suggest a protective effect of more years of education on a lower dementia risk with a particular critical threshold of completing >10 years of education. Further, the findings highlight that, when examining risks and protective factors of dementia, a careful consideration of the underlying definitions and operationalisation approaches is required. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Cataract Avoidance With Proton Therapy in Ocular Melanomas.

PubMed

Thariat, Juliette; Jacob, Sophie; Caujolle, Jean-Pierre; Maschi, Celia; Baillif, Stéphanie; Angellier, Gaelle; Mathis, Thibaud; Rosier, Laurence; Carnicer, Adela; Hérault, Joel; Salleron, Julia

2017-10-01

The lens is a radiosensitive organ. Any dose of cephalic irradiation can give rise to radiation-induced cataracts. Contrary to other forms of radiotherapy, proton therapy (PT) can spare all or part of the lens due to accurate dose deposition. We investigated whether a lens-sparing approach was relevant to avoid cataracts in uveal melanoma patients. Patients were referred for PT from onco-ophthalmologists of private and academic institutions. Patients without preexisting cataracts or implants were entered in a prospective database. Dose thresholds responsible for cataracts were investigated in volumes of lens or lens periphery. Lens opacifications and de novo vision-impairing cataracts (VICs) had biannual follow up by ophthalmologists blinded to lens dose. Correlations between dose-volume relationships and VICs were assessed using univariate/multivariate regressions. Between 1991 and 2015, 1696 uveal melanoma patients were consecutively treated with PT. After a median follow up of 48 months, 14.4% and 8.7% of patients had cataracts and VIC within median times of 19 and 28 months, respectively. Median values of mean lens and lens periphery doses were 1.1 (radiobiologically effective [RBE] dose in photon-equivalent grays [GyRBE]) and 6.5 GyRBE, respectively. The lens received no dose in 25% of the patients. At an irradiated lens volume of ≤5%, there was no significantly increased risk for VIC below a dose of 10 GyRBE. A lens-sparing approach is feasible and results not only in reduced need for cataract surgery but also in better fundus-based tumor control. Reassessment of radioprotection rules for lens dose thresholds may follow.

Catastrophic health expenditure and its determinants in Kenya slum communities.

PubMed

Buigut, Steven; Ettarh, Remare; Amendah, Djesika D

2015-05-14

In Kenya, where 60 to 80% of the urban residents live in informal settlements (frequently referred to as slums), out-of-pocket (OOP) payments account for more than a third of national health expenditures. However, little is known on the extent to which these OOP payments are associated with personal or household financial catastrophe in the slums. This paper seeks to examine the incidence and determinants of catastrophic health expenditure among urban slum communities in Kenya. We use a unique dataset on informal settlement residents in Kenya and various approaches that relate households OOP payments for healthcare to total expenditures adjusted for subsistence, or income. We classified households whose OOP was in excess of a predefined threshold as facing catastrophic health expenditures (CHE), and identified the determinants of CHE using multivariate logistic regression analysis. The results indicate that the proportion of households facing CHE varies widely between 1.52% and 28.38% depending on the method and the threshold used. A core set of variables were found to be key determinants of CHE. The number of working adults in a household and membership in a social safety net appear to reduce the risk of catastrophic expenditure. Conversely, seeking care in a public or private hospital increases the risk of CHE. This study suggests that a substantial proportion of residents of informal settlements in Kenya face CHE and would likely forgo health care they need but cannot afford. Mechanisms that pool risk and cost (insurance) are needed to protect slum residents from CHE and improve equity in health care access and payment.

Cumulative Effects of HIV Illness and Caring for Children Orphaned by AIDS on Anxiety Symptoms Among Adults Caring for Children in HIV-Endemic South Africa

PubMed Central

Cluver, Lucie; Casale, Marisa; Lane, Tyler

2014-01-01

Abstract Adults caring for children in HIV-endemic communities are at risk for poor psychological outcomes. However, we still have a limited understanding of how various HIV impacts—including caregiver's own HIV illness, responsibilities of caring for a child orphaned by AIDS, or both—affect psychological outcomes among caregivers. Furthermore, few studies have explored the relationship between stigma, HIV, and psychological outcomes among caregivers of children in HIV-endemic communities. A cross-sectional survey conducted from 2009 to 2010 assessed anxiety among 2477 caregivers of children in HIV-endemic South Africa. Chi-square tested differences in anxiety among caregivers living with HIV, caregivers of a child orphaned by AIDS, and caregivers affected with both conditions. Multivariate logistic regressions identified whether the relationship between HIV impacts and anxiety remained after controlling for socio-demographic co-factors. Mediation analysis tested the relationship between stigma, HIV, and anxiety. The odds of meeting threshold criteria for clinically relevant anxiety symptoms were two and a half times greater among caregivers living with HIV compared to nonaffected caregivers. The odds of meeting threshold criteria for clinically relevant anxiety symptoms were greatest among caregivers living with HIV and caring for a child orphaned by AIDS. Exposure to AIDS-related stigma partially mediated the relationship between HIV and anxiety. Interventions are needed to address caregiver psychological health, particularly among caregivers affected with both conditions of living with HIV and caring for a child orphaned by AIDS. PMID:24901465

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Integration of community structure data reveals observable effects below sediment guideline thresholds in a large estuary.

PubMed

Tremblay, Louis A; Clark, Dana; Sinner, Jim; Ellis, Joanne I

2017-09-20

The sustainable management of estuarine and coastal ecosystems requires robust frameworks due to the presence of multiple physical and chemical stressors. In this study, we assessed whether ecological health decline, based on community structure composition changes along a pollution gradient, occurred at levels below guideline threshold values for copper, zinc and lead. Canonical analysis of principal coordinates (CAP) was used to characterise benthic communities along a metal contamination gradient. The analysis revealed changes in benthic community distribution at levels below the individual guideline values for the three metals. These results suggest that field-based measures of ecological health analysed with multivariate tools can provide additional information to single metal guideline threshold values to monitor large systems exposed to multiple stressors.

Association between hepatitis B co-infection and elevated liver stiffness among HIV-infected adults in Lusaka, Zambia.

PubMed

Vinikoor, Michael J; Mulenga, Lloyd; Siyunda, Alice; Musukuma, Kalo; Chilengi, Roma; Moore, Carolyn Bolton; Chi, Benjamin H; Davies, Mary-Ann; Egger, Matthias; Wandeler, Gilles

2016-11-01

To describe liver disease epidemiology among HIV-infected individuals in Zambia. We recruited HIV-infected adults (≥18 years) at antiretroviral therapy initiation at two facilities in Lusaka. Using vibration controlled transient elastography, we assessed liver stiffness, a surrogate for fibrosis/cirrhosis, and analysed liver stiffness measurements (LSM) according to established thresholds (>7.0 kPa for significant fibrosis and >11.0 kPa for cirrhosis). All participants underwent standardised screening for potential causes of liver disease including chronic hepatitis B (HBV) and C virus co-infection, herbal medicine, and alcohol use. We used multivariable logistic regression to identify factors associated with elevated liver stiffness. Among 798 HIV-infected patients, 651 had a valid LSM (median age, 34 years; 53% female). HBV co-infection (12%) and alcohol use disorders (41%) were common and hepatitis C virus co-infection (<1%) was rare. According to LSM, 75 (12%) had significant fibrosis and 13 (2%) had cirrhosis. In multivariable analysis, HBV co-infection as well as male sex, increased age and WHO clinical stage 3 or 4 were independently associated with LSM >7.0 kPa (all P < 0.05). HBV co-infection was the only independent risk factor for LSM >11.0 kPa. Among HIV-HBV patients, those with elevated ALT and HBV viral load were more likely to have significant liver fibrosis than patients with normal markers of HBV activity. HBV co-infection was the most important risk factor for liver fibrosis and cirrhosis and should be diagnosed early in HIV care to optimise treatment outcomes. © 2016 John Wiley & Sons Ltd.

X-ray tomography using the full complex index of refraction.

PubMed

Nielsen, M S; Lauridsen, T; Thomsen, M; Jensen, T H; Bech, M; Christensen, L B; Olsen, E V; Hviid, M; Feidenhans'l, R; Pfeiffer, F

2012-10-07

We report on x-ray tomography using the full complex index of refraction recorded with a grating-based x-ray phase-contrast setup. Combining simultaneous absorption and phase-contrast information, the distribution of the full complex index of refraction is determined and depicted in a bivariate graph. A simple multivariable threshold segmentation can be applied offering higher accuracy than with a single-variable threshold segmentation as well as new possibilities for the partial volume analysis and edge detection. It is particularly beneficial for low-contrast systems. In this paper, this concept is demonstrated by experimental results.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Robust detection, isolation and accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Emami-Naeini, A.; Akhter, M. M.; Rock, S. M.

1986-01-01

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Identification of ecological thresholds from variations in phytoplankton communities among lakes: contribution to the definition of environmental standards.

PubMed

Roubeix, Vincent; Danis, Pierre-Alain; Feret, Thibaut; Baudoin, Jean-Marc

2016-04-01

In aquatic ecosystems, the identification of ecological thresholds may be useful for managers as it can help to diagnose ecosystem health and to identify key levers to enable the success of preservation and restoration measures. A recent statistical method, gradient forest, based on random forests, was used to detect thresholds of phytoplankton community change in lakes along different environmental gradients. It performs exploratory analyses of multivariate biological and environmental data to estimate the location and importance of community thresholds along gradients. The method was applied to a data set of 224 French lakes which were characterized by 29 environmental variables and the mean abundances of 196 phytoplankton species. Results showed the high importance of geographic variables for the prediction of species abundances at the scale of the study. A second analysis was performed on a subset of lakes defined by geographic thresholds and presenting a higher biological homogeneity. Community thresholds were identified for the most important physico-chemical variables including water transparency, total phosphorus, ammonia, nitrates, and dissolved organic carbon. Gradient forest appeared as a powerful method at a first exploratory step, to detect ecological thresholds at large spatial scale. The thresholds that were identified here must be reinforced by the separate analysis of other aquatic communities and may be used then to set protective environmental standards after consideration of natural variability among lakes.

Basis Selection for Wavelet Regression

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Lau, Sonie (Technical Monitor)

1998-01-01

A wavelet basis selection procedure is presented for wavelet regression. Both the basis and the threshold are selected using cross-validation. The method includes the capability of incorporating prior knowledge on the smoothness (or shape of the basis functions) into the basis selection procedure. The results of the method are demonstrated on sampled functions widely used in the wavelet regression literature. The results of the method are contrasted with other published methods.

Robust regression on noisy data for fusion scaling laws

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be; Laboratoire de Physique des Plasmas de l'ERM - Laboratorium voor Plasmafysica van de KMS

2014-11-15

We introduce the method of geodesic least squares (GLS) regression for estimating fusion scaling laws. Based on straightforward principles, the method is easily implemented, yet it clearly outperforms established regression techniques, particularly in cases of significant uncertainty on both the response and predictor variables. We apply GLS for estimating the scaling of the L-H power threshold, resulting in estimates for ITER that are somewhat higher than predicted earlier.

Dependence of intravoxel incoherent motion diffusion MR threshold b-value selection for separating perfusion and diffusion compartments and liver fibrosis diagnostic performance.

PubMed

Wáng, Yì Xiáng J; Li, Yáo T; Chevallier, Olivier; Huang, Hua; Leung, Jason Chi Shun; Chen, Weitian; Lu, Pu-Xuan

2018-01-01

Background Intravoxel incoherent motion (IVIM) tissue parameters depend on the threshold b-value. Purpose To explore how threshold b-value impacts PF ( f), D slow ( D), and D fast ( D*) values and their performance for liver fibrosis detection. Material and Methods Fifteen healthy volunteers and 33 hepatitis B patients were included. With a 1.5-T magnetic resonance (MR) scanner and respiration gating, IVIM data were acquired with ten b-values of 10, 20, 40, 60, 80, 100, 150, 200, 400, and 800 s/mm 2 . Signal measurement was performed on the right liver. Segmented-unconstrained analysis was used to compute IVIM parameters and six threshold b-values in the range of 40-200 s/mm 2 were compared. PF, D slow , and D fast values were placed along the x-axis, y-axis, and z-axis, and a plane was defined to separate volunteers from patients. Results Higher threshold b-values were associated with higher PF measurement; while lower threshold b-values led to higher D slow and D fast measurements. The dependence of PF, D slow , and D fast on threshold b-value differed between healthy livers and fibrotic livers; with the healthy livers showing a higher dependence. Threshold b-value = 60 s/mm 2 showed the largest mean distance between healthy liver datapoints vs. fibrotic liver datapoints, and a classification and regression tree showed that a combination of PF (PF < 9.5%), D slow (D slow  < 1.239 × 10 -3 mm 2 /s), and D fast (D fast  < 20.85 × 10 -3 mm 2 /s) differentiated healthy individuals and all individual fibrotic livers with an area under the curve of logistic regression (AUC) of 1. Conclusion For segmented-unconstrained analysis, the selection of threshold b-value = 60 s/mm 2 improves IVIM differentiation between healthy livers and fibrotic livers.

The association of plasma glucose, BHBA, and NEFA with postpartum uterine diseases, fertility, and milk production of Holstein dairy cows.

PubMed

Bicalho, M L S; Marques, E C; Gilbert, R O; Bicalho, R C

2017-01-15

The objective of this study was to investigate the association between the metabolic indicators such as nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and glucose during the transition period and the development of uterine diseases. In total, 181 Holstein dairy cows were enrolled in the study. Plasma glucose, NEFA, and BHBA concentrations were measured at -50, -6, 3, 7, and 14 days relative to parturition. All cows enrolled in the study were evaluated for retained placenta (RP), metritis, and endometritis. Metritis and RP were diagnosed and treated by trained farm personnel. Clinical endometritis was evaluated by a veterinarian at 35 days in milk using a Metricheck device. We found plasma glucose concentration to be associated with the occurrence of metritis and clinical endometritis. Moreover, cows with an increased calving-to-conception interval (>150 days) presented higher plasma glucose concentrations than cows that became pregnant within the first 150 days, whereas BHBA and NEFA were not associated with the occurrence of any uterine disorder. Receiver operating characteristic (ROC) curves were used in an attempt to determine the cow-level critical thresholds for the occurrence of metritis, and endometritis. In addition, pairwise comparisons of area under the curve (AUC) of ROC curves for the critical thresholds for glucose, BHBA, and NEFA predicting the same uterine disease were performed. Glucose at 3 days in milk was the best predictor for metritis and endometritis diagnosis, with AUC values of 0.66 and 0.67, respectively. Multivariable logistic regressions were performed and showed that cows with higher levels of glucose at Day 3 were at 6.6 times higher odds of being diagnosed with metritis, and 3.5 times higher odds of developing clinical endometritis, compared with cows with lower glucose levels. Finally, a simple linear regression analysis demonstrated a negative correlation between daily milk yield in the first and second weeks of lactation and plasma glucose concentrations measured at Days 7 and 14, respectively. Concentrations of NEFA and BHBA were not found to be associated with milk production. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Spatio-Temporal Regression Based Clustering of Precipitation Extremes in a Presence of Systematically Missing Covariates

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Martius, Olivia; Horenko, Illia

2017-04-01

Regression based Generalized Pareto Distribution (GPD) models are often used to describe the dynamics of hydrological threshold excesses relying on the explicit availability of all of the relevant covariates. But, in real application the complete set of relevant covariates might be not available. In this context, it was shown that under weak assumptions the influence coming from systematically missing covariates can be reflected by a nonstationary and nonhomogenous dynamics. We present a data-driven, semiparametric and an adaptive approach for spatio-temporal regression based clustering of threshold excesses in a presence of systematically missing covariates. The nonstationary and nonhomogenous behavior of threshold excesses is describes by a set of local stationary GPD models, where the parameters are expressed as regression models, and a non-parametric spatio-temporal hidden switching process. Exploiting nonparametric Finite Element time-series analysis Methodology (FEM) with Bounded Variation of the model parameters (BV) for resolving the spatio-temporal switching process, the approach goes beyond strong a priori assumptions made is standard latent class models like Mixture Models and Hidden Markov Models. Additionally, the presented FEM-BV-GPD provides a pragmatic description of the corresponding spatial dependence structure by grouping together all locations that exhibit similar behavior of the switching process. The performance of the framework is demonstrated on daily accumulated precipitation series over 17 different locations in Switzerland from 1981 till 2013 - showing that the introduced approach allows for a better description of the historical data.

Development of Decision Support Formulas for the Prediction of Bladder Outlet Obstruction and Prostatic Surgery in Patients With Lower Urinary Tract Symptom/Benign Prostatic Hyperplasia: Part I, Development of the Formula and its Internal Validation.

PubMed

Choo, Min Soo; Yoo, Changwon; Cho, Sung Yong; Jeong, Seong Jin; Jeong, Chang Wook; Ku, Ja Hyeon; Oh, Seung-June

2017-04-01

As the elderly population increases, a growing number of patients have lower urinary tract symptom (LUTS)/benign prostatic hyperplasia (BPH). The aim of this study was to develop decision support formulas and nomograms for the prediction of bladder outlet obstruction (BOO) and for BOO-related surgical decision-making, and to validate them in patients with LUTS/BPH. Patient with LUTS/BPH between October 2004 and May 2014 were enrolled as a development cohort. The available variables included age, International Prostate Symptom Score, free uroflowmetry, postvoid residual volume, total prostate volume, and the results of a pressure-flow study. A causal Bayesian network analysis was used to identify relevant parameters. Using multivariate logistic regression analysis, formulas were developed to calculate the probabilities of having BOO and requiring prostatic surgery. Patients between June 2014 and December 2015 were prospectively enrolled for internal validation. Receiver operating characteristic curve analysis, calibration plots, and decision curve analysis were performed. A total of 1,179 male patients with LUTS/BPH, with a mean age of 66.1 years, were included as a development cohort. Another 253 patients were enrolled as an internal validation cohort. Using multivariate logistic regression analysis, 2 and 4 formulas were established to estimate the probabilities of having BOO and requiring prostatic surgery, respectively. Our analysis of the predictive accuracy of the model revealed area under the curve values of 0.82 for BOO and 0.87 for prostatic surgery. The sensitivity and specificity were 53.6% and 87.0% for BOO, and 91.6% and 50.0% for prostatic surgery, respectively. The calibration plot indicated that these prediction models showed a good correspondence. In addition, the decision curve analysis showed a high net benefit across the entire spectrum of probability thresholds. We established nomograms for the prediction of BOO and BOO-related prostatic surgery in patients with LUTS/BPH. Internal validation of the nomograms demonstrated that they predicted both having BOO and requiring prostatic surgery very well.

Development and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy.

PubMed

Chen, Rui; Xie, Liping; Xue, Wei; Ye, Zhangqun; Ma, Lulin; Gao, Xu; Ren, Shancheng; Wang, Fubo; Zhao, Lin; Xu, Chuanliang; Sun, Yinghao

2016-09-01

Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men. Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals. Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa. CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds. Copyright © 2016 Elsevier Inc. All rights reserved.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

Have the temperature time series a structural change after 1998?

NASA Astrophysics Data System (ADS)

Werner, Rolf; Valev, Dimitare; Danov, Dimitar

2012-07-01

The global and hemisphere temperature GISS and Hadcrut3 time series were analysed for structural changes. We postulate the continuity of the preceding temperature function depending from the time. The slopes are calculated for a sequence of segments limited by time thresholds. We used a standard method, the restricted linear regression with dummy variables. We performed the calculations and tests for different number of thresholds. The thresholds are searched continuously in determined time intervals. The F-statistic is used to obtain the time points of the structural changes.

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

PubMed

Mani, Ashutosh; Rao, Marepalli; James, Kelley; Bhattacharya, Amit

2015-01-01

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Forward masking as a mechanism of automatic gain control in odontocete biosonar: a psychophysical study.

PubMed

Supin, Alexander Ya; Nachtigall, Paul E; Breese, Marlee

2008-07-01

In a false killer whale Pseudorca crassidens, echo perception thresholds were measured using a go/no-go psychophysical paradigm and one-up-one-down staircase procedure. Computer controlled echoes were electronically synthesized pulses that were played back through a transducer and triggered by whale emitted biosonar pulses. The echo amplitudes were proportional to biosonar pulse amplitudes; echo levels were specified in terms of the attenuation of the echo sound pressure level near the animal's head relative to the source level of the biosonar pulses. With increasing echo delay, the thresholds (echo attenuation factor) decreased from -49.3 dB at 2 ms to -79.5 dB at 16 ms, with a regression slope of -9.5 dB per delay doubling (-31.5 dB per delay decade). At the longer delays, the threshold remained nearly constant around -80.4 dB. Levels of emitted pulses slightly increased with delay prolongation (threshold decrease), with a regression slope of 3.2 dB per delay doubling (10.7 dB per delay decade). The echo threshold dependence on delay is interpreted as a release from forward masking by the preceding emitted pulse. This release may compensate for the echo level decrease with distance, thus keeping the echo sensation level for the animal near constant within a certain distance range.

Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches

USGS Publications Warehouse

Brooks, Wesley R.; Fienen, Michael N.; Corsi, Steven R.

2013-01-01

At public beaches, it is now common to mitigate the impact of water-borne pathogens by posting a swimmer's advisory when the concentration of fecal indicator bacteria (FIB) exceeds an action threshold. Since culturing the bacteria delays public notification when dangerous conditions exist, regression models are sometimes used to predict the FIB concentration based on readily-available environmental measurements. It is hard to know which environmental parameters are relevant to predicting FIB concentration, and the parameters are usually correlated, which can hurt the predictive power of a regression model. Here the method of partial least squares (PLS) is introduced to automate the regression modeling process. Model selection is reduced to the process of setting a tuning parameter to control the decision threshold that separates predicted exceedances of the standard from predicted non-exceedances. The method is validated by application to four Great Lakes beaches during the summer of 2010. Performance of the PLS models compares favorably to that of the existing state-of-the-art regression models at these four sites.

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E

2002-01-01

Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of post-fire crown damage mortality thresholds in ponderosa pine

Treesearch

James F. Fowler; Carolyn Hull Sieg; Joel McMillin; Kurt K. Allen; Jose F. Negron; Linda L. Wadleigh; John A. Anhold; Ken E. Gibson

2010-01-01

Previous research has shown that crown scorch volume and crown consumption volume are the major predictors of post-fire mortality in ponderosa pine. In this study, we use piecewise logistic regression models of crown scorch data from 6633 trees in five wildfires from the Intermountain West to locate a mortality threshold at 88% scorch by volume for trees with no crown...

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada).

PubMed

Blais, Martin; Fernet, Mylène; Proulx-Boucher, Karène; Lebouché, Bertrand; Rodrigue, Carl; Lapointe, Normand; Otis, Joanne; Samson, Johanne

2015-01-01

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

A statistical human rib cage geometry model accounting for variations by age, sex, stature and body mass index.

PubMed

Shi, Xiangnan; Cao, Libo; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2014-07-18

In this study, we developed a statistical rib cage geometry model accounting for variations by age, sex, stature and body mass index (BMI). Thorax CT scans were obtained from 89 subjects approximately evenly distributed among 8 age groups and both sexes. Threshold-based CT image segmentation was performed to extract the rib geometries, and a total of 464 landmarks on the left side of each subject׳s ribcage were collected to describe the size and shape of the rib cage as well as the cross-sectional geometry of each rib. Principal component analysis and multivariate regression analysis were conducted to predict rib cage geometry as a function of age, sex, stature, and BMI, all of which showed strong effects on rib cage geometry. Except for BMI, all parameters also showed significant effects on rib cross-sectional area using a linear mixed model. This statistical rib cage geometry model can serve as a geometric basis for developing a parametric human thorax finite element model for quantifying effects from different human attributes on thoracic injury risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Catastrophic household expenditure for health care in a low-income society: a study from Nouna District, Burkina Faso.

PubMed Central

Su, Tin Tin; Kouyaté, Bocar; Flessa, Steffen

2006-01-01

OBJECTIVE: To quantify the extent of catastrophic household health care expenditure and determine the factors responsible for it in Nouna District, Burkina Faso. METHODS: We used the Nouna Health District Household Survey to collect data on 800 households during 2000-01 for our analysis. The determinants of household catastrophic expenditure were identified by multivariate logistic regression method. FINDINGS: Even at very low levels of health care utilization and modest amount of health expenditure, 6-15% of total households in Nouna District incurred catastrophic health expenditure. The key determinants of catastrophic health expenditure were economic status, household health care utilization especially for modern medical care, illness episodes in an adult household member and presence of a member with chronic illness. CONCLUSION: We conclude that the poorest members of the community incurred catastrophic health expenses. Setting only one threshold/cut-off value to determine catastrophic health expenses may result in inaccurate estimation leading to misinterpretation of important factors. Our findings have important policy implications and can be used to ensure better access to health services and a higher degree of financial protection for low-income groups against the economic impact of illness. PMID:16501711

False-negative rate cannot be reduced by lowering the haemoglobin concentration cut-off in colorectal cancer screening using faecal immunochemical test.

PubMed

Ibañez-Sanz, Gemma; Garcia, Montse; Milà, Núria; Rodríguez-Moranta, Francisco; Binefa, Gemma; Gómez-Matas, Javier; Benito, Llúcia; Padrol, Isabel; Barenys, Mercè; Moreno, Victor

2017-09-01

The aim of this study was to analyse false-negative (FN) results of the faecal immunochemical test (FIT) and its determinants in a colorectal cancer screening programme in Catalonia. We carried out a cross-sectional study among 218 screenees with a negative FIT result who agreed to undergo a colonoscopy. A false-negative result was defined as the detection, at colonoscopy, of intermediate/high-risk polyps or colorectal cancer in a patient with a previous negative FIT (<20 µgHb/g). Multivariate logistic regression models were constructed to identify sociodemographic (sex, age) and screening variables (quantitative faecal haemoglobin, colonoscopy findings) related to FN results. Adjusted odds ratios and their 95% confidence intervals were estimated. There were 15.6% FN FIT results. Faecal haemoglobin was undetected in 45.5% of these results and was below 4 µgHb/g in 94.0% of the individuals with a FN result. About 60% of the lesions were located in the proximal colon, whereas the expected percentage was 30%. Decreasing the positivity threshold of FIT does not increase the detection rate of advanced neoplasia, but may increase the costs and potential adverse effects.

Returning from the acidotic abyss: Mortality in trauma patients with a pH < 7.0.

PubMed

Ross, Samuel W; Thomas, Bradley W; Christmas, A Britton; Cunningham, Kyle W; Sing, Ronald F

2017-12-01

We hypothesized that a pH of <7.0 on presentation would correlate with almost universal mortality in trauma patients. A retrospective cohort study was performed on a Level I trauma center registry from 2013 to 2014. Hospital mortality was the primary outcome, which was compared by pH cohort (<7.0 or ≥7.0) using standard univariate statistics and multivariate logistic regression. There were 593 patients included in the analysis: 66 in <7.0, 527 in ≥7.0. Mortality was 3× higher in the <7.0 pH cohort (62.1 vs. 20.3%; p < 0.0001), however there was no threshold for a pH below which there was 100% mortality. After controlling for these confounding variables, initial pH was found to be an independent predictor of inpatient mortality: pH < 7.0 (OR 6.33, 3.29-12.19; p < 0.0001). This data indicates that while patients with severe acidosis are at increased risk for mortality, a pH < 7.0 is still recoverable in select cases. Copyright © 2017 Elsevier Inc. All rights reserved.

The prognostic impact of clinical and CT parameters in patients with pontine hemorrhage.

PubMed

Dziewas, Rainer; Kremer, Marion; Lüdemann, Peter; Nabavi, Darius G; Dräger, Bianca; Ringelstein, Bernd

2003-01-01

In patients with pontine hemorrhage (PH), an accurate prognostic assessment is critical for establishing a reasonable therapeutic approach. The initial clinical symptoms and computed tomography (CT) features were analyzed with multivariate regression analysis in 39 consecutive patients with PH. PHs were classified into three types: (1) large paramedian, (2) basal or basotegmental and (3) lateral tegmental, and the hematomas' diameters were measured. The patients' outcome was evaluated. Twenty-seven patients (69%) died and 12 (31%) survived for more than 1 year after PH. The symptom most predictive of death was coma on admission. The large paramedian type of PH predicted a poor prognosis, whereas the lateral tegmental type was associated with a favorable outcome. The transverse hematoma diameter was also related to outcome, with the threshold value found to be 20 mm. We conclude that PH outcome can be estimated best by combining the CT parameters 'large paramedian PH' and 'transverse diameter >/=20 mm' with the clinical variable 'coma on admission'. Survival is unlikely if all 3 features are present, whereas survival may be expected if only 1 or none of these features is found. Copyright 2003 S. Karger AG, Basel

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth.

PubMed

Menon, Ramkumar; Bhat, Geeta; Saade, George R; Spratt, Heidi

2014-04-01

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

Estimating the Impact of the PROMISE Scholarship Using Propensity Score Weighted Frontier Fuzzy Regression Discontinuity Design

ERIC Educational Resources Information Center

Shobo, Yetty; Wong, Jen D.; Bell, Angie

2014-01-01

Regression discontinuity (RD), an "as good as randomized," research design is increasingly prominent in education research in recent years; the design gets eligible quasi-experimental designs as close as possible to experimental designs by using a stated threshold on a continuous baseline variable to assign individuals to a…

Regression Discontinuity Designs: A Guide to Practice. NBER Working Paper No. 13039

ERIC Educational Resources Information Center

Imbens, Guido; Lemieux, Thomas

2007-01-01

In Regression Discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell (1960). With the exception of…

The Effects of School Wide Bonuses on Student Achievement: Regression Discontinuity Evidence from North Carolina

ERIC Educational Resources Information Center

Lauen, Douglas Lee

2011-01-01

This study examines the incentive effects of North Carolina's practice of awarding performance bonuses on test score achievement on the state tests. Bonuses were awarded based solely on whether a school exceeds a threshold on a continuous performance metric. The study uses a sharp regression discontinuity design, an approach with strong internal…

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Squeezing Interval Change From Ordinal Panel Data: Latent Growth Curves With Ordinal Outcomes

ERIC Educational Resources Information Center

Mehta, Paras D.; Neale, Michael C.; Flay, Brian R.

2004-01-01

A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the…

Regional rainfall thresholds for landslide occurrence using a centenary database

NASA Astrophysics Data System (ADS)

Vaz, Teresa; Luís Zêzere, José; Pereira, Susana; Cruz Oliveira, Sérgio; Garcia, Ricardo A. C.; Quaresma, Ivânia

2018-04-01

This work proposes a comprehensive method to assess rainfall thresholds for landslide initiation using a centenary landslide database associated with a single centenary daily rainfall data set. The method is applied to the Lisbon region and includes the rainfall return period analysis that was used to identify the critical rainfall combination (cumulated rainfall duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds are assessed and calibrated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, these thresholds may be used with acceptable confidence up to 50 km from the rain gauge. The rainfall thresholds obtained using linear and potential regression perform well in ROC metrics. However, the intermediate thresholds based on the probability of landslide events established in the zone between the lower-limit threshold and the upper-limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Sources of stress and psychological morbidity among undergraduate physiotherapy students.

PubMed

Walsh, J M; Feeney, C; Hussey, J; Donnellan, C

2010-09-01

Professional education can be a stressful experience for some individuals, and may impact negatively on emotional well-being and academic performance. Psychological morbidity and associated sources of stress have not been investigated extensively in physiotherapy students. This study explored sources of stress, psychological morbidity and possible associations between these variables in undergraduate physiotherapy students. A questionnaire-based survey. The Undergraduate Sources of Stress Questionnaire was used to identify sources of stress, and the General Health Questionnaire-12 (GHQ-12) was used to rate the prevalence of psychological morbidity, using a conservative GHQ threshold of 3 to 4 to determine probable 'cases'. Uni- and multivariate tests of correlation were used to analyse the data. An Irish educational institution. One hundred and twenty-five physiotherapy undergraduate students. More than one-quarter of all students (27%) scored above the GHQ threshold, indicating probable psychological morbidity. This is higher than the level of psychological morbidity reported by the general population. Regression analysis showed that academic (beta=0.31, P<0.001) and personal (beta=0.50, P<0.001) sources of stress subscales were significant coefficients, explaining 48% of the variance in psychological morbidity after controlling for part-time employment and hours spent studying. Individual significant items from these subscales were stressful events (beta=0.24, P=0.004), mood (beta=0.43, P< or =0.001) and overall level of stress (beta=0.35, P< or =0.001). The results highlighted the emotional vulnerability of a significant proportion of physiotherapy students, with academic and personal issues being the greatest concern. While personal causes of stress such as stressful events and mood are more difficult to control, manipulation of curricular factors may have positive effects on academic sources of stress. Copyright 2010 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.

Is There a Safe Lipoaspirate Volume? A Risk Assessment Model of Liposuction Volume as a Function of Body Mass Index.

PubMed

Chow, Ian; Alghoul, Mohammed S; Khavanin, Nima; Hanwright, Philip J; Mayer, Kristen E; Hume, Keith M; Murphy, Robert X; Gutowski, Karol A; Kim, John Y S

2015-09-01

No concrete data exist to support a specific volume at which liposuction becomes unsafe; surgeons rely on their own estimates, professional organization advisories, or institutional or government-imposed restrictions. This study represents the first attempt to quantify the comprehensive risk associated with varying liposuction volumes and its interaction with body mass index. Suction-assisted lipectomies were identified from the Tracking Operations and Outcomes for Plastic Surgeons database. Multivariate regression models incorporating the interaction between liposuction volume and body mass index were used to assess the influence of liposuction volume on complications and to develop a tool that returns a single adjusted odds ratio for any combination of body mass index and liposuction volume. Recursive partitioning was used to determine whether exceeding a threshold in liposuction volume per body mass index unit significantly increased complications. Sixty-nine of 4534 patients (1.5 percent) meeting inclusion criteria experienced a postoperative complication. Liposuction volume and body mass index were significant independent risk factors for complications. With progressively higher volumes, increasing body mass index reduced risk (OR, 0.99; 95 percent CI, 0.98 to 0.99; p = 0.007). Liposuction volumes in excess of 100 ml per unit of body mass index were an independent predictor of complications (OR, 4.58; 95 percent CI, 2.60 to 8.05; p < 0.001). Liposuction by board-certified plastic surgeons is safe, with a low risk of life-threatening complications. Traditional liposuction volume thresholds do not accurately convey individualized risk. The authors' risk assessment model demonstrates that volumes in excess of 100 ml per unit of body mass index confer an increased risk of complications. Therapeutic, III.

Variations in BMI and prevalence of health risks in diverse racial and ethnic populations.

PubMed

Stommel, Manfred; Schoenborn, Charlotte A

2010-09-01

When examining health risks associated with the BMI, investigators often rely on the customary BMI thresholds of the 1995 World Health Organization report. However, within-interval variations in morbidity and mortality can be substantial, and the thresholds do not necessarily correspond to identifiable risk increases. Comparing the prevalence of hypertension, diabetes, coronary heart disease (CHD), asthma, and arthritis among non-Hispanic whites, blacks, East Asians and Hispanics, we examine differences in the BMI-health-risk relationships for small BMI increments. The analysis is based on 11 years of data of the National Health Interview Survey (NHIS), with a sample size of 337,375 for the combined 1997-2007 Sample Adult. The analysis uses multivariate logistic regression models, employing a nonparametric approach to modeling the BMI-health-risk relationship, while relying on narrowly defined BMI categories. Rising BMI levels are associated with higher levels of chronic disease burdens in four major racial and ethnic groups, even after adjusting for many socio-demographic characteristics and three important health-related behaviors (smoking, physical activity, alcohol consumption). For all population groups, except East Asians, a modestly higher disease risk was noted for persons with a BMI <20 compared with persons with BMI in the range of 20-21. Using five chronic conditions as risk criteria, a categorization of the BMI into normal weight, overweight, or obesity appears arbitrary. Although the prevalence of disease risks differs among racial and ethnic groups regardless of BMI levels, the evidence presented here does not support the notion that the BMI-health-risk profile of East Asians and others warrants race-specific BMI cutoff points.

Technical feasibility and validation of a coronary artery calcium scoring system using CT coronary angiography images.

PubMed

Pavitt, Christopher W; Harron, Katie; Lindsay, Alistair C; Zielke, Sayeh; Ray, Robin; Gordon, Daniel; Rubens, Michael B; Padley, Simon P; Nicol, Edward D

2016-05-01

We validate a novel CT coronary angiography (CCTA) coronary calcium scoring system. Calcium was quantified on CCTA images using a new patient-specific attenuation threshold: mean + 2SD of intra-coronary contrast density (HU). Using 335 patient data sets a conversion factor (CF) for predicting CACS from CCTA scores (CCTAS) was derived and validated in a separate cohort (n = 168). Bland-Altman analysis and weighted kappa for MESA centiles and Agatston risk groupings were calculated. Multivariable linear regression yielded a CF: CACS = (1.185 × CCTAS) + (0.002 × CCTAS × attenuation threshold). When applied to CCTA data sets there was excellent correlation (r = 0.95; p < 0.0001) and agreement (mean difference -10.4 [95% limits of agreement -258.9 to 238.1]) with traditional calcium scores. Agreement was better for calcium scores below 500; however, MESA percentile agreement was better for high risk patients. Risk stratification was excellent (Agatston groups k = 0.88 and MESA centiles k = 0.91). Eliminating the dedicated CACS scan decreased patient radiation exposure by approximately one-third. CCTA calcium scores can accurately predict CACS using a simple, individualized, semiautomated approach reducing acquisition time and radiation exposure when evaluating patients for CAD. This method is not affected by the ROI location, imaging protocol, or tube voltage strengthening its clinical applicability. • Coronary calcium scores can be reliably determined on contrast-enhanced cardiac CT • This score can accurately risk stratify patients • Elimination of a dedicated calcium scan reduces patient radiation by a third.

Olfactory Cleft Endoscopy Scale correlates with olfactory metrics in patients with chronic rhinosinusitis

PubMed Central

Soler, Zachary M.; Hyer, J. Madison; Karnezis, Tom T.; Schlosser, Rodney J.

2015-01-01

Introduction Olfactory loss affects a majority of patients with chronic rhinosinusitis (CRS). Traditional objective measures of disease severity, including endoscopy scales, focus upon the paranasal sinuses and often have weak correlation to olfaction. Methods Adults with CRS were prospectively evaluated by blinded reviewers with a novel Olfactory Cleft Endoscopy Scale (OCES) that evaluated discharge, polyps, edema, crusting and scarring of the olfactory cleft. Objective olfactory function was assessed using “Sniffin’ Sticks testing, including composite threshold-discrimination-identification (TDI) scores. Olfactory-specific quality-of-life was evaluated using the short modified version of the Questionnaire of Olfactory Disorders (QOD-NS). Inter- and intra-rater reliability was assessed among 3 reviewers for OCES grading. Multivariate linear regression was then used to test associations between OCES scores and measures of olfaction, controlling for potential confounding factors. Results The OCES score was evaluated in 38 patients and had a high overall reliability (ICC=0.92; 95% CI: 0.91–0.96). The OCES significantly correlated with objective olfaction as measured by TDI score (p<0.001), with TDI score falling by 1.13 points for every 1 point increase in OCES score. Similar significant associations were found for threshold, discrimination, and identification scores (p<0.003 for all) after controlling for age, gender, race, and reviewer/review. The OCES was also highly associated with patient-reported QOD-NS scores (p=0.009). Conclusion A novel olfactory cleft endoscopy scale shows high reliability and correlates with both objective and patient-reported olfaction in patients with CRS. Further studies to determine prognostic value and responsiveness to change are warranted. PMID:26718315

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Multivariate Analysis of Seismic Field Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire

USGS Publications Warehouse

Andy, Caroline; Fahnestock, Maria Florencia; Lombard, Melissa; Hayes, Laura; Bryce, Julie; Ayotte, Joseph

2017-01-01

Three existing multivariate logistic regression models were assessed using new data to evaluate the capacity of the models to correctly predict the probability of groundwater arsenic concentrations exceeding the threshold values of 1, 5, and 10 micrograms per liter (µg/L) in New Hampshire, USA. A recently released testing dataset includes arsenic concentrations from groundwater samples collected in 2004–2005 from a mix of 367 public-supply and private domestic wells. The use of this dataset to test three existing logistic regression models demonstrated enhanced overall predictive accuracy for the 5 and 10 μg/L models. Overall accuracies of 54.8, 76.3, and 86.4 percent were reported for the 1, 5, and 10 μg/L models, respectively. The state was divided by counties into northwest and southeast regions. Regional differences in accuracy were identified; models had an average accuracy of 83.1 percent for the counties in the northwest and 63.7 percent in the southeast. This is most likely due to high model specificity in the northwest and regional differences in arsenic occurrence. Though these models have limitations, they allow for arsenic hazard assessment across the region. The introduction of well-type (public or private), well depth, and casing length as explanatory variables may be appropriate measures to improve model performance. Our findings indicate that the original models generalize to the testing dataset, and should continue to serve as an important vehicle of preventative public health that may be applied to other groundwater contaminants in New Hampshire.

Active Duty - U.S. Army Noise Induced Hearing Injury Surveillance Calendar Years 2009-2013

DTIC Science & Technology

2014-06-01

rates for sensorineural hearing loss, significant threshold shift, tinnitus , and Noise-Induced Hearing Loss. The intention is to monitor the morbidity...surveillance. These code groups include sensorineural hearing loss (SNHL), significant threshold shift (STS), noise-induced hearing loss (NIHL) and tinnitus ... Tinnitus ) was analyzed using a regression model to determine the trend of incidence rates from 2007 to the current year. Statistical significance of a

Predictive factors of synchronous colorectal peritoneal metastases: Development of a nomogram and study of its utilities using decision curve analysis.

PubMed

Mo, Shaobo; Dai, Weixing; Xiang, Wenqiang; Li, Qingguo; Wang, Renjie; Cai, Guoxiang

2018-05-03

The objective of this study was to summarize the clinicopathological and molecular features of synchronous colorectal peritoneal metastases (CPM). We then combined clinical and pathological variables associated with synchronous CPM into a nomogram and confirmed its utilities using decision curve analysis. Synchronous metastatic colorectal cancer (mCRC) patients who received primary tumor resection and underwent KRAS, NRAS, and BRAF gene mutation detection at our center from January 2014 to September 2015 were included in this retrospective study. An analysis was performed to investigate the clinicopathological and molecular features for independent risk factors of synchronous CPM and to subsequently develop a nomogram for synchronous CPM based on multivariate logistic regression. Model performance was quantified in terms of calibration and discrimination. We studied the utility of the nomogram using decision curve analysis. In total, 226 patients were diagnosed with synchronous mCRC, of whom 50 patients (22.1%) presented with CPM. After uni- and multivariate analysis, a nomogram was built based on tumor site, histological type, age, and T4 status. The model had good discrimination with an area under the curve (AUC) at 0.777 (95% CI 0.703-0.850) and adequate calibration. By decision curve analysis, the model was shown to be relevant between thresholds of 0.10 and 0.66. Synchronous CPM is more likely to happen to patients with age ≤60, right-sided primary lesions, signet ring cell cancer or T4 stage. This is the first nomogram to predict synchronous CPM. To ensure generalizability, this model needs to be externally validated. Copyright © 2018 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.

Analysis of acute radiation-induced esophagitis in non-small-cell lung cancer patients using the Lyman NTCP model.

PubMed

Zhu, Jian; Zhang, Zi-Cheng; Li, Bao-Sheng; Liu, Min; Yin, Yong; Yu, Jin-Ming; Luo, Li-Min; Shu, Hua-Zhong; De Crevoisier, Renaud

2010-12-01

To analyze acute esophagitis (AE) in a Chinese population receiving 3D conformal radiotherapy (3DCRT) for non-small cell lung cancer (NSCLC), combined or not with chemotherapy (CT), using the Lyman-Kutcher-Burman (LKB) normal tissue complication probability (NTCP) model. 157 Chinese patients (pts) presented with NSCLC received 3DCRT: alone (34 pts) or combined with sequential CT (59 pts) (group 1) or with concomitant CT (64 pts) (group 2). Parameters (TD(50), n, and m) of the LKB NTCP model predicting for>grade 2 AE (RTOG grading) were identified using maximum likelihood analysis. Univariate and multivariate analyses using a binary regression logistic model were performed to identify patient, tumor and dosimetric predictors of AE. Grade 2 or 3 AE occurred in 24% and 52% of pts in group 1 and 2, respectively (p<0.001). For the 93 group 1 pts, the fitted LKB model parameters were: m=0.15, n=0.29 and TD(50)=46 Gy. For the 64 group 2 pts, the parameters were: m=0.42, n=0.09 and TD(50)=36 Gy. In multivariate analysis, the only significant predictors of AE were: NTCP (p<0.001) and V(50), as continuous variable (RR=1.03, p=0.03) or being more than a threshold value of 11% (RR=3.6, p=0.009). A LKB NTCP model has been established to predict AE in a Chinese population, receiving thoracic RT, alone or combined with CT. The parameters of the models appear slightly different than the previous one described in Western countries, with a lower volume effect for Chinese patients. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Prognosis of Adults with Borderline Left Ventricular Ejection Fraction

PubMed Central

Tsao, Connie W.; Lyass, Asya; Larson, Martin G.; Cheng, Susan; Lam, Carolyn S.P.; Aragam, Jayashri R.; Benjamin, Emelia J.; Vasan, Ramachandran S.

2016-01-01

Objectives To examine the association of borderline LVEF of 50-55% with cardiovascular morbidity and mortality in a community-based cohort. Background Guidelines stipulate left ventricular ejection fraction (LVEF) >55% as normal, but the optimal threshold, if any, remains uncertain. The prognosis of a “borderline” LVEF, 50-55%, is unknown. Methods We evaluated Framingham Heart Study participants who underwent echocardiography between 1979 and 2008 (n=10,270 person-observations, mean age 60 years, 57% women). Using pooled data with up to 12 years of follow-up and multivariable Cox regression, we evaluated the associations of borderline LVEF, and continuous LVEF to the risk of developing a composite outcome (heart failure [HF] or death; primary outcome) and incident HF (secondary outcome). Results During follow-up (median 7.9 years), 355 participants developed HF and 1070 died. Among participants with LVEF 50-55% (prevalence 3.5%), rates of the composite outcome and HF were 0.24 and 0.13 per 10 years follow-up, respectively, versus 0.16 and 0.05 in those having normal LVEF. In multivariable-adjusted analyses, LVEF 50-55% was associated with increased risk of the composite outcome (Hazards ratio [HR] 1.37, 95% CI 1.05-1.80) and HF (HR 2.15, 95% CI 1.41-3.28). There was a linear inverse relationship of continuous LVEF with the composite outcome (HR per 5 LVEF% decrement: 1.12, 95% CI 1.07-1.16) and HF (HR 1.23 per 5 LVEF% decrement, 95% CI 1.15-1.32). Conclusions Individuals with LVEF of 50-55% in the community have greater risk for morbidity and mortality relative to those with LVEF >55%. Additional studies are warranted to elucidate their optimal management. PMID:27256754

The Effective Dynamic Ranges for Glaucomatous Visual Field Progression With Standard Automated Perimetry and Stimulus Sizes III and V.

PubMed

Wall, Michael; Zamba, Gideon K D; Artes, Paul H

2018-01-01

It has been shown that threshold estimates below approximately 20 dB have little effect on the ability to detect visual field progression in glaucoma. We aimed to compare stimulus size V to stimulus size III, in areas of visual damage, to confirm these findings by using (1) a different dataset, (2) different techniques of progression analysis, and (3) an analysis to evaluate the effect of censoring on mean deviation (MD). In the Iowa Variability in Perimetry Study, 120 glaucoma subjects were tested every 6 months for 4 years with size III SITA Standard and size V Full Threshold. Progression was determined with three complementary techniques: pointwise linear regression (PLR), permutation of PLR, and linear regression of the MD index. All analyses were repeated on "censored'' datasets in which threshold estimates below a given criterion value were set to equal the criterion value. Our analyses confirmed previous observations that threshold estimates below 20 dB contribute much less to visual field progression than estimates above this range. These findings were broadly similar with stimulus sizes III and V. Censoring of threshold values < 20 dB has relatively little impact on the rates of visual field progression in patients with mild to moderate glaucoma. Size V, which has lower retest variability, performs at least as well as size III for longitudinal glaucoma progression analysis and appears to have a larger useful dynamic range owing to the upper sensitivity limit being higher.

An investigation of mechanical nociceptive thresholds in dogs with hind limb joint pain compared to healthy control dogs.

PubMed

Harris, L K; Whay, H R; Murrell, J C

2018-04-01

This study investigated the effects of osteoarthritis (OA) on somatosensory processing in dogs using mechanical threshold testing. A pressure algometer was used to measure mechanical thresholds in 27 dogs with presumed hind limb osteoarthritis and 28 healthy dogs. Mechanical thresholds were measured at the stifles, radii and sternum, and were correlated with scores from an owner questionnaire and a clinical checklist, a scoring system that quantified clinical signs of osteoarthritis. The effects of age and bodyweight on mechanical thresholds were also investigated. Multiple regression models indicated that, when bodyweight was taken into account, dogs with presumed osteoarthritis had lower mechanical thresholds at the stifles than control dogs, but not at other sites. Non-parametric correlations showed that clinical checklist scores and questionnaire scores were negatively correlated with mechanical thresholds at the stifles. The results suggest that mechanical threshold testing using a pressure algometer can detect primary, and possibly secondary, hyperalgesia in dogs with presumed osteoarthritis. This suggests that the mechanical threshold testing protocol used in this study might facilitate assessment of somatosensory changes associated with disease progression or response to treatment. Copyright © 2017. Published by Elsevier Ltd.

Experimental and environmental factors affect spurious detection of ecological thresholds

USGS Publications Warehouse

Daily, Jonathan P.; Hitt, Nathaniel P.; Smith, David; Snyder, Craig D.

2012-01-01

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Comprehensive ripeness-index for prediction of ripening level in mangoes by multivariate modelling of ripening behaviour

NASA Astrophysics Data System (ADS)

Eyarkai Nambi, Vijayaram; Thangavel, Kuladaisamy; Manickavasagan, Annamalai; Shahir, Sultan

2017-01-01

Prediction of ripeness level in climacteric fruits is essential for post-harvest handling. An index capable of predicting ripening level with minimum inputs would be highly beneficial to the handlers, processors and researchers in fruit industry. A study was conducted with Indian mango cultivars to develop a ripeness index and associated model. Changes in physicochemical, colour and textural properties were measured throughout the ripening period and the period was classified into five stages (unripe, early ripe, partially ripe, ripe and over ripe). Multivariate regression techniques like partial least square regression, principal component regression and multi linear regression were compared and evaluated for its prediction. Multi linear regression model with 12 parameters was found more suitable in ripening prediction. Scientific variable reduction method was adopted to simplify the developed model. Better prediction was achieved with either 2 or 3 variables (total soluble solids, colour and acidity). Cross validation was done to increase the robustness and it was found that proposed ripening index was more effective in prediction of ripening stages. Three-variable model would be suitable for commercial applications where reasonable accuracies are sufficient. However, 12-variable model can be used to obtain more precise results in research and development applications.

Factors related to the joint probability of flooding on paired streams

USGS Publications Warehouse

Koltun, G.F.; Sherwood, J.M.

1998-01-01

The factors related to the joint probabilty of flooding on paired streams were investigated and quantified to provide information to aid in the design of hydraulic structures where the joint probabilty of flooding is an element of the design criteria. Stream pairs were considered to have flooded jointly at the design-year flood threshold (corresponding to the 2-, 10-, 25-, or 50-year instantaneous peak streamflow) if peak streamflows at both streams in the pair were observed or predicted to have equaled or exceeded the threshold on a given calendar day. Daily mean streamflow data were used as a substitute for instantaneous peak streamflow data to determine which flood thresholds were equaled or exceeded on any given day. Instantaneous peak streamflow data, when available, were used preferentially to assess flood-threshold exceedance. Daily mean streamflow data for each stream were paired with concurrent daily mean streamflow data at the other streams. Observed probabilities of joint flooding, determined for the 2-, 10-, 25-, and 50-year flood thresholds, were computed as the ratios of the total number of days when streamflows at both streams concurrently equaled or exceeded their flood thresholds (events) to the total number of days where streamflows at either stream equaled or exceeded its flood threshold (trials). A combination of correlation analyses, graphical analyses, and logistic-regression analyses were used to identify and quantify factors associated with the observed probabilities of joint flooding (event-trial ratios). The analyses indicated that the distance between drainage area centroids, the ratio of the smaller to larger drainage area, the mean drainage area, and the centroid angle adjusted 30 degrees were the basin characteristics most closely associated with the joint probabilty of flooding on paired streams in Ohio. In general, the analyses indicated that the joint probabilty of flooding decreases with an increase in centroid distance and increases with increases in drainage area ratio, mean drainage area, and centroid angle adjusted 30 degrees. Logistic-regression equations were developed, which can be used to estimate the probability that streamflows at two streams jointly equal or exceed the 2-year flood threshold given that the streamflow at one of the two streams equals or exceeds the 2-year flood threshold. The logistic-regression equations are applicable to stream pairs in Ohio (and border areas of adjacent states) that are unregulated, free of significant urban influences, and have characteristics similar to those of the 304 gaged stream pairs used in the logistic-regression analyses. Contingency tables were constructed and analyzed to provide information about the bivariate distribution of floods on paired streams. The contingency tables showed that the percentage of trials in which both streams in the pair concurrently flood at identical recurrence-interval ranges generally increased as centroid distances decreased and was greatest for stream pairs with adjusted centroid angles greater than or equal to 60 degrees and drainage area ratios greater than or equal to 0.01. Also, as centroid distance increased, streamflow at one stream in the pair was more likely to be in a less than 2-year recurrence-interval range when streamflow at the second stream was in a 2-year or greater recurrence-interval range.

TG study of the Li0.4Fe2.4Zn0.2O4 ferrite synthesis

NASA Astrophysics Data System (ADS)

Lysenko, E. N.; Nikolaev, E. V.; Surzhikov, A. P.

2016-02-01

In this paper, the kinetic analysis of Li-Zn ferrite synthesis was studied using thermogravimetry (TG) method through the simultaneous application of non-linear regression to several measurements run at different heating rates (multivariate non-linear regression). Using TG-curves obtained for the four heating rates and Netzsch Thermokinetics software package, the kinetic models with minimal adjustable parameters were selected to quantitatively describe the reaction of Li-Zn ferrite synthesis. It was shown that the experimental TG-curves clearly suggest a two-step process for the ferrite synthesis and therefore a model-fitting kinetic analysis based on multivariate non-linear regressions was conducted. The complex reaction was described by a two-step reaction scheme consisting of sequential reaction steps. It is established that the best results were obtained using the Yander three-dimensional diffusion model at the first stage and Ginstling-Bronstein model at the second step. The kinetic parameters for lithium-zinc ferrite synthesis reaction were found and discussed.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Shifts in the relationship between motor unit recruitment thresholds versus derecruitment thresholds during fatigue.

PubMed

Stock, Matt S; Mota, Jacob A

2017-12-01

Muscle fatigue is associated with diminished twitch force amplitude. We examined changes in the motor unit recruitment versus derecruitment threshold relationship during fatigue. Nine men (mean age = 26 years) performed repeated isometric contractions at 50% maximal voluntary contraction (MVC) knee extensor force until exhaustion. Surface electromyographic signals were detected from the vastus lateralis, and were decomposed into their constituent motor unit action potential trains. Motor unit recruitment and derecruitment thresholds and firing rates at recruitment and derecruitment were evaluated at the beginning, middle, and end of the protocol. On average, 15 motor units were studied per contraction. For the initial contraction, three subjects showed greater recruitment thresholds than derecruitment thresholds for all motor units. Five subjects showed greater recruitment thresholds than derecruitment thresholds for only low-threshold motor units at the beginning, with a mean cross-over of 31.6% MVC. As the muscle fatigued, many motor units were derecruited at progressively higher forces. In turn, decreased slopes and increased y-intercepts were observed. These shifts were complemented by increased firing rates at derecruitment relative to recruitment. As the vastus lateralis fatigued, the central nervous system's compensatory adjustments resulted in a shift of the regression line of the recruitment versus derecruitment threshold relationship. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

Threshold and resilience management of coupled urbanization and water environmental system in the rapidly changing coastal region.

PubMed

Li, Yangfan; Li, Yi; Wu, Wei

2016-01-01

The concept of thresholds shows important implications for environmental and resource management. Here we derived potential landscape thresholds which indicated abrupt changes in water quality or the dividing points between exceeding and failing to meet national surface water quality standards for a rapidly urbanizing city on the Eastern Coast in China. The analysis of landscape thresholds was based on regression models linking each of the seven water quality variables to each of the six landscape metrics for this coupled land-water system. We found substantial and accelerating urban sprawl at the suburban areas between 2000 and 2008, and detected significant nonlinear relations between water quality and landscape pattern. This research demonstrated that a simple modeling technique could provide insights on environmental thresholds to support more-informed decision making in land use, water environmental and resilience management. Copyright © 2015 Elsevier Ltd. All rights reserved.

Motor Unit Interpulse Intervals During High Force Contractions.

PubMed

Stock, Matt S; Thompson, Brennan J

2016-01-01

We examined the means, medians, and variability for motor-unit interpulse intervals (IPIs) during voluntary, high force contractions. Eight men (mean age = 22 years) attempted to perform isometric contractions at 90% of their maximal voluntary contraction force while bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis and vastus medialis muscles. Surface EMG signal decomposition was used to determine the recruitment thresholds and IPIs of motor units that demonstrated accuracy levels ≥ 96.0%. Motor units with high recruitment thresholds demonstrated longer mean IPIs, but the coefficients of variation were similar across all recruitment thresholds. Polynomial regression analyses indicated that for both muscles, the relationship between the means and standard deviations of the IPIs was linear. The majority of IPI histograms were positively skewed. Although low-threshold motor units were associated with shorter IPIs, the variability among motor units with differing recruitment thresholds was comparable.

Clinical management provided by board-certificated physiatrists in early rehabilitation is a significant determinant of functional improvement in acute stroke patients: a retrospective analysis of Japan rehabilitation database.

PubMed

Kinoshita, Shoji; Kakuda, Wataru; Momosaki, Ryo; Yamada, Naoki; Sugawara, Hidekazu; Watanabe, Shu; Abo, Masahiro

2015-05-01

Early rehabilitation for acute stroke patients is widely recommended. We tested the hypothesis that clinical outcome of stroke patients who receive early rehabilitation managed by board-certificated physiatrists (BCP) is generally better than that provided by other medical specialties. Data of stroke patients who underwent early rehabilitation in 19 acute hospitals between January 2005 and December 2013 were collected from the Japan Rehabilitation Database and analyzed retrospectively. Multivariate linear regression analysis using generalized estimating equations method was performed to assess the association between Functional Independence Measure (FIM) effectiveness and management provided by BCP in early rehabilitation. In addition, multivariate logistic regression analysis was also performed to assess the impact of management provided by BCP in acute phase on discharge destination. After setting the inclusion criteria, data of 3838 stroke patients were eligible for analysis. BCP provided early rehabilitation in 814 patients (21.2%). Both the duration of daily exercise time and the frequency of regular conferencing were significantly higher for patients managed by BCP than by other specialties. Although the mortality rate was not different, multivariate regression analysis showed that FIM effectiveness correlated significantly and positively with the management provided by BCP (coefficient, .35; 95% confidence interval [CI], .012-.059; P < .005). In addition, multivariate logistic analysis identified clinical management by BCP as a significant determinant of home discharge (odds ratio, 1.24; 95% CI, 1.08-1.44; P < .005). Our retrospective cohort study demonstrated that clinical management provided by BCP in early rehabilitation can lead to functional recovery of acute stroke. Copyright © 2015 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Novel Analog For Muscle Deconditioning

NASA Technical Reports Server (NTRS)

Ploutz-Snyder, Lori; Ryder, Jeff; Buxton, Roxanne; Redd, Elizabeth; Scott-Pandorf, Melissa; Hackney, Kyle; Fiedler, James; Bloomberg, Jacob

2010-01-01

Existing models of muscle deconditioning are cumbersome and expensive (ex: bedrest). We propose a new model utilizing a weighted suit to manipulate strength, power or endurance (function) relative to body weight (BW). Methods: 20 subjects performed 7 occupational astronaut tasks while wearing a suit weighted with 0-120% of BW. Models of the full relationship between muscle function/BW and task completion time were developed using fractional polynomial regression and verified by the addition of pre- and post-flight astronaut performance data using the same tasks. Spline regression was used to identify muscle function thresholds below which task performance was impaired. Results: Thresholds of performance decline were identified for each task. Seated egress & walk (most difficult task) showed thresholds of: leg press (LP) isometric peak force/BW of 18 N/kg, LP power/BW of 18 W/kg, LP work/ BW of 79 J/kg, knee extension (KE) isokinetic/BW of 6 Nm/Kg and KE torque/BW of 1.9 Nm/kg. Conclusions: Laboratory manipulation of strength / BW has promise as an appropriate analog for spaceflight-induced loss of muscle function for predicting occupational task performance and establishing operationally relevant exercise targets.

Poly I:C-induced fever elevates threshold for shivering but reduces thermosensitivity in rabbits.

PubMed

Tøien, Ø; Mercer, J B

1995-05-01

Shivering threshold and thermosensitivity were determined in six conscious rabbits at ambient temperature (Ta) 20 and 10 degrees C before and at six different times after saline injection (0.15 ml iv) and polyriboinosinic-polyribocytidylic acid (poly I:C)-induced fever (5 micrograms/kg iv). Thermosensitivity was calculated by regression of metabolic heat production (M) and hypothalamic temperature (Thypo) during short periods (5-10 min) of square-wave cooling. Heat was extracted with a chronically implanted intravascular heat exchanger. Shivering threshold was calculated as the Thypo at which the thermosensitivity line crossed resting M as measured in afebrile animals at Ta 20 degrees C. There were negligible changes in shivering threshold and thermosensitivity in saline-injected rabbits. In the febrile animals, shivering threshold generally followed the shape of the biphasic fever response. At Ta 20 degrees C, shivering threshold was higher than regulated Thypo during the initial rising phase of fever and was lower during recovery. At Ta 10 degrees C the shivering thresholds were always higher than regulated Thypo except during recovery. Thermosensitivity was reduced by 30-41% during fever.

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Cellulose I crystallinity determination using FT-Raman spectroscopy : univariate and multivariate methods

Treesearch

Umesh P. Agarwal; Richard S. Reiner; Sally A. Ralph

2010-01-01

Two new methods based on FT–Raman spectroscopy, one simple, based on band intensity ratio, and the other using a partial least squares (PLS) regression model, are proposed to determine cellulose I crystallinity. In the simple method, crystallinity in cellulose I samples was determined based on univariate regression that was first developed using the Raman band...

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Per capita community-level effects of an invasive grass, Microstegium vimineum, on vegetation in mesic forests in northern Mississippi (USA)

Treesearch

J. Stephen Brewer

2010-01-01

Quantifying per capita impacts of invasive species on resident communities requires integrating regression analyses with experiments under natural conditions. Using multivariate and univariate approaches, I regressed the abundance of 105 resident species of groundcover plants and tree seedlings against the abundance and height of an invasive grass, Microstegium...

Quantitative analysis of binary polymorphs mixtures of fusidic acid by diffuse reflectance FTIR spectroscopy, diffuse reflectance FT-NIR spectroscopy, Raman spectroscopy and multivariate calibration.

PubMed

Guo, Canyong; Luo, Xuefang; Zhou, Xiaohua; Shi, Beijia; Wang, Juanjuan; Zhao, Jinqi; Zhang, Xiaoxia

2017-06-05

Vibrational spectroscopic techniques such as infrared, near-infrared and Raman spectroscopy have become popular in detecting and quantifying polymorphism of pharmaceutics since they are fast and non-destructive. This study assessed the ability of three vibrational spectroscopy combined with multivariate analysis to quantify a low-content undesired polymorph within a binary polymorphic mixture. Partial least squares (PLS) regression and support vector machine (SVM) regression were employed to build quantitative models. Fusidic acid, a steroidal antibiotic, was used as the model compound. It was found that PLS regression performed slightly better than SVM regression in all the three spectroscopic techniques. Root mean square errors of prediction (RMSEP) were ranging from 0.48% to 1.17% for diffuse reflectance FTIR spectroscopy and 1.60-1.93% for diffuse reflectance FT-NIR spectroscopy and 1.62-2.31% for Raman spectroscopy. The results indicate that diffuse reflectance FTIR spectroscopy offers significant advantages in providing accurate measurement of polymorphic content in the fusidic acid binary mixtures, while Raman spectroscopy is the least accurate technique for quantitative analysis of polymorphs. Copyright © 2017 Elsevier B.V. All rights reserved.

Regression analysis for LED color detection of visual-MIMO system

NASA Astrophysics Data System (ADS)

Banik, Partha Pratim; Saha, Rappy; Kim, Ki-Doo

2018-04-01

Color detection from a light emitting diode (LED) array using a smartphone camera is very difficult in a visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to determine the LED color using a smartphone camera by applying regression analysis. We employ a multivariate regression model to identify the LED color. After taking a picture of an LED array, we select the LED array region, and detect the LED using an image processing algorithm. We then apply the k-means clustering algorithm to determine the number of potential colors for feature extraction of each LED. Finally, we apply the multivariate regression model to predict the color of the transmitted LEDs. In this paper, we show our results for three types of environmental light condition: room environmental light, low environmental light (560 lux), and strong environmental light (2450 lux). We compare the results of our proposed algorithm from the analysis of training and test R-Square (%) values, percentage of closeness of transmitted and predicted colors, and we also mention about the number of distorted test data points from the analysis of distortion bar graph in CIE1931 color space.

Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

NASA Astrophysics Data System (ADS)

Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

2015-11-01

One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Relationships among measures of physical activity and hearing in African Americans: The Jackson Heart Study.

PubMed

Haas, Patrick J; Bishop, Charles E; Gao, Yan; Griswold, Michael E; Schweinfurth, John M

2016-10-01

To evaluate the relationships among measures of physical activity and hearing in the Jackson Heart Study. Prospective cohort study. We assessed hearing on 1,221 Jackson Heart Study participants who also had validated physical activity questionnaire data on file. Hearing thresholds were measured across frequency octaves from 250 to 8,000 Hz, and various frequency pure-tone averages (PTAs) were constructed, including PTA4 (average of 500, 1,000, 2,000, and 4,000 Hz), PTA-high (average of 4,000 and 8,000 Hz), PTA-mid (average of 1,000 and 2,000 Hz), and PTA-low (average of 250 and 500 Hz). Hearing loss was defined for pure tones and pure-tone averages as >25 dB HL in either ear and averaged between the ears. Associations between physical activity and hearing were estimated using linear regression, reporting changes in decibel hearing level, and logistic regression, reporting odds ratios (OR) of hearing loss. Physical activity exhibited a statistically significant but small inverse relationship with PTA4, -0.20 dB HL per doubling of activity (95% confidence interval [CI]: -0.35, -0.04; P = .016), as well as with PTA-low and pure tones at 250, 2,000, and 4,000 Hz in adjusted models. Multivariable logistic regression modeling supported a decrease in the odds of high-frequency hearing loss among participants who reported at least some moderate weekly physical activity (PTA-high, OR: 0.69 [95% CI: 0.52, 0.92]; P = .011 and 4000 Hz, OR: 0.75 [95% CI: 0.57, 0.99]; P = .044). Our study provides further evidence that physical activity is related to better hearing; however, the clinical significance of this relationship cannot be estimated given the nature of the cross-sectional study design. 2b Laryngoscope, 126:2376-2381, 2016. © 2016 The American Laryngological, Rhinological and Otological Society, Inc.

A Quasi-Experiment To Study the Impact of Vancomycin Area under the Concentration-Time Curve-Guided Dosing on Vancomycin-Associated Nephrotoxicity

PubMed Central

Finch, Natalie A.; Zasowski, Evan J.; Murray, Kyle P.; Mynatt, Ryan P.; Zhao, Jing J.; Yost, Raymond; Pogue, Jason M.

2017-01-01

ABSTRACT Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC24) target of ≥400 mg · h/liter. Many patients with trough concentrations in this range have AUC24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure. PMID:28923869

A Quasi-Experiment To Study the Impact of Vancomycin Area under the Concentration-Time Curve-Guided Dosing on Vancomycin-Associated Nephrotoxicity.

PubMed

Finch, Natalie A; Zasowski, Evan J; Murray, Kyle P; Mynatt, Ryan P; Zhao, Jing J; Yost, Raymond; Pogue, Jason M; Rybak, Michael J

2017-12-01

Evidence suggests that maintenance of vancomycin trough concentrations at between 15 and 20 mg/liter, as currently recommended, is frequently unnecessary to achieve the daily area under the concentration-time curve (AUC 24 ) target of ≥400 mg · h/liter. Many patients with trough concentrations in this range have AUC 24 values in excess of the therapeutic threshold and within the exposure range associated with nephrotoxicity. On the basis of this, the Detroit Medical Center switched from trough concentration-guided dosing to AUC-guided dosing to minimize potentially unnecessary vancomycin exposure. The primary objective of this analysis was to assess the impact of this intervention on vancomycin-associated nephrotoxicity in a single-center, retrospective quasi-experiment of hospitalized adult patients receiving intravenous vancomycin from 2014 to 2015. The primary analysis compared the incidence of nephrotoxicity between patients monitored by assessment of the AUC 24 and those monitored by assessment of the trough concentration. Multivariable logistic and Cox proportional hazards regression examined the independent association between the monitoring strategy and nephrotoxicity. Secondary analysis compared vancomycin exposures (total daily dose, AUC, and trough concentrations) between monitoring strategies. Overall, 1,280 patients were included in the analysis. After adjusting for severity of illness, comorbidity, duration of vancomycin therapy, and concomitant receipt of nephrotoxins, AUC-guided dosing was independently associated with lower nephrotoxicity by both logistic regression (odds ratio, 0.52; 95% confidence interval [CI], 0.34 to 0.80; P = 0.003) and Cox proportional hazards regression (hazard ratio, 0.53; 95% CI, 0.35 to 0.78; P = 0.002). AUC-guided dosing was associated with lower total daily vancomycin doses, AUC values, and trough concentrations. Vancomycin AUC-guided dosing was associated with reduced nephrotoxicity, which appeared to be a result of reduced vancomycin exposure. Copyright © 2017 American Society for Microbiology.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Analysis of Smartphone Interruptions on Academic General Internal Medicine Wards

PubMed Central

C.Wu, Robert

2017-01-01

Summary Introduction Hospital-based medical services are increasingly utilizing team-based pagers and smartphones to streamline communications. However, an unintended consequence may be higher volumes of interruptions potentially leading to medical error. There is likely a level at which interruptions are excessive and cause a ‘crisis mode’ climate. Methods We retrospectively collected phone, text messaging, and email interruptions directed to hospital-assigned smartphones on eight General Internal Medicine (GIM) teams at two tertiary care centres in Toronto, Ontario from April 2013 to September 2014. We also calculated the number of times these interruptions exceeded a pre-specified threshold per hour, termed ‘crisis mode’, defined as at least five interruptions in 30 minutes. We analyzed the correlation between interruptions and date, site, and patient volumes. Results A total of 187,049 interruptions were collected over an 18-month period. Daily weekday interruptions rose sharply in the morning, peaking between 11 AM to 12 PM and measuring 4.8 and 3.7 mean interruptions/hour at each site, respectively. Mean daily interruptions per team totaled 46.2 ± 3.6 at Site 1 and 39.2 ± 4.2 at Site 2. The ‘crisis mode’ threshold was exceeded, on average, 2.3 times/day per GIM team during weekdays. In a multivariable linear regression analysis, site (β6.43 CI95% 5.44 – 7.42, p<0.001), day of the week (with Friday having the most interruptions) (β0.481 CI95% 0.236 – 0.730, p<0.05) and patient census (β1.55 CI95% 1.42 – 1.67, p<0.05) were all predictive of daily interruption volume although there was a significant interaction effect between site and patient census (β-0.941 CI95% -1.18 – -0.703, p<0.05). Conclusion Interruptions were related to site-specific features, including volume, suggesting that future interventions should target the culture of individual hospitals. Excessive interruptions may have implications for patient safety especially when exceeding a maximal threshold over short periods of time. PMID:28066851

Analysis of Smartphone Interruptions on Academic General Internal Medicine Wards. Frequent Interruptions may cause a 'Crisis Mode' Work Climate.

PubMed

Vaisman, Alon; Wu, Robert C

2017-01-04

Hospital-based medical services are increasingly utilizing team-based pagers and smartphones to streamline communications. However, an unintended consequence may be higher volumes of interruptions potentially leading to medical error. There is likely a level at which interruptions are excessive and cause a 'crisis mode' climate. We retrospectively collected phone, text messaging, and email interruptions directed to hospital-assigned smartphones on eight General Internal Medicine (GIM) teams at two tertiary care centres in Toronto, Ontario from April 2013 to September 2014. We also calculated the number of times these interruptions exceeded a pre-specified threshold per hour, termed 'crisis mode', defined as at least five interruptions in 30 minutes. We analyzed the correlation between interruptions and date, site, and patient volumes. A total of 187,049 interruptions were collected over an 18-month period. Daily weekday interruptions rose sharply in the morning, peaking between 11 AM to 12 PM and measuring 4.8 and 3.7 mean interruptions/hour at each site, respectively. Mean daily interruptions per team totaled 46.2 ± 3.6 at Site 1 and 39.2 ± 4.2 at Site 2. The 'crisis mode' threshold was exceeded, on average, 2.3 times/day per GIM team during weekdays. In a multivariable linear regression analysis, site (β6.43 CI95% 5.44 - 7.42, p<0.001), day of the week (with Friday having the most interruptions) (β0.481 CI95% 0.236 - 0.730, p<0.05) and patient census (β1.55 CI95% 1.42 - 1.67, p<0.05) were all predictive of daily interruption volume although there was a significant interaction effect between site and patient census (β-0.941 CI95% -1.18 - -0.703, p<0.05). Interruptions were related to site-specific features, including volume, suggesting that future interventions should target the culture of individual hospitals. Excessive interruptions may have implications for patient safety especially when exceeding a maximal threshold over short periods of time.

Tumor volume in insignificant prostate cancer: increasing threshold gains increasing risk.

PubMed

Schiffmann, Jonas; Connan, Judith; Salomon, Georg; Boehm, Katharina; Beyer, Burkhard; Schlomm, Thorsten; Tennstedt, Pierre; Sauter, Guido; Karakiewicz, Pierre I; Graefen, Markus; Huland, Hartwig

2015-01-01

An increased tumor volume threshold (<2.5 ml) is suggested to define insignificant prostate cancer (iPCa). We hypothesize that an increasing tumor volume within iPCa patients increases the risk of biochemical recurrence (BCR) after radical prostatectomy (RP). We relied on RP patients treated between 1992 and 2008. Multivariable Cox regression analyses predicting BCR within patients harboring favorable pathological characteristics (≤pT2, pN0/Nx, Gleason 3 + 3). Kaplan-Meier analysis was performed for BCR-free survival within iPCa patients (≤pT2, pN0/Nx, Gleason 3 + 3, tumor volume: <0.5 vs. 0.5-2.49 ml). From 1,829 patients, 141 (7.7%) and 310 (16.9%) harbored iPCa (tumor volume: <0.5 vs. 0.5-2.49 ml), respectively. Of those, 21 (14.9%) versus 31 (10.0%) had PSA >10 ng/ml. Tumor volume achieved independent predictor status for BCR. Specifically, iPCa patients with increasing tumor volume (0.5-2.49 ml) were at higher risk of BCR after RP than those with tumor volume <0.5 ml (HR: 8.8, 95% CI: 1.2-65.9, P = 0.04). Kaplan-Meier analysis recorded superior BCR-free survival in iPCa patients with lower tumor volume (<0.5 ml) (log-rank P = 0.009). The 10-year cancer-specific death rate was 0 versus 0.5%. Contemporary iPCa definition incorporates intermediate and high-risk patients (PSA: 10-20 and >20 ng/ml). Despite most favorable pathological characteristics, iPCa patients are not devoid of BCR after RP. Moreover, iPCa patients were at higher risk of BCR, when increasing tumor volume up to 2.49 ml was at play. Taken together the contemporary concept of iPCa is suboptimal. Especially, an increased tumor volume threshold for defining iPCa cannot be recommended according to our data. Clinicians might take these considerations into account during decision-making process. © 2014 Wiley Periodicals, Inc.

Effectiveness of the hearing conservation program: Change in hearing threshold shift incidence among industrial workers, 1978 to 2003

NASA Astrophysics Data System (ADS)

Davies, Hugh

2005-04-01

Hearing conservation programs (HCP) are widely employed in preventing noise-induced hearing loss, but studies of their effectiveness have been rare. The impact of the implementation of hearing conservation programs was assessed in a large group of highly noise-exposed blue-collar workers by investigating time-trends in hearing-threshold shift incidence. Serial annual audiograms for employees of 14 British Columbia lumber mills for the period 1978 to 2003 were obtained from local regulatory-agency archives. Audiograms and concomitant otological medical histories were linked to subjects' work histories and noise exposure data. Multivariable Cox proportional hazard models were used to model the incidence of hearing threshold shift while controlling for age, baseline level of hearing loss, and other potential confounders. A total of 109257 audiograms were associated with 10590 subjects. Mean noise exposure in this group was 91.4 dBA(A). Mean interval between hearing tests was 566 days and mean age at first threshold shift was 44. Forty-six percent of subjects had at least one OSHA significant threshold shift during follow up. Preliminary analyses indicated a trend toward lower incidence of threshold shifts over the study period, with incidence in 5 approximately equal 5-year periods from 1978 to 2003 being 3.2%, 6.6%, 4.9%, 4.3% and 2.4%, respectively.

Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state.

PubMed

Carbonell, F; Bellec, P; Shmuel, A

2014-02-01

The effect of regressing out the global average signal (GAS) in resting state fMRI data has become a concern for interpreting functional connectivity analyses. It is not clear whether the reported anti-correlations between the Default Mode and the Dorsal Attention Networks are intrinsic to the brain, or are artificially created by regressing out the GAS. Here we introduce a concept, Impact of the Global Average on Functional Connectivity (IGAFC), for quantifying the sensitivity of seed-based correlation analyses to the regression of the GAS. This voxel-wise IGAFC index is defined as the product of two correlation coefficients: the correlation between the GAS and the fMRI time course of a voxel, times the correlation between the GAS and the seed time course. This definition enables the calculation of a threshold at which the impact of regressing-out the GAS would be large enough to introduce spurious negative correlations. It also yields a post-hoc impact correction procedure via thresholding, which eliminates spurious correlations introduced by regressing out the GAS. In addition, we introduce an Artificial Negative Correlation Index (ANCI), defined as the absolute difference between the IGAFC index and the impact threshold. The ANCI allows a graded confidence scale for ranking voxels according to their likelihood of showing artificial correlations. By applying this method, we observed regions in the Default Mode and Dorsal Attention Networks that were anti-correlated. These findings confirm that the previously reported negative correlations between the Dorsal Attention and Default Mode Networks are intrinsic to the brain and not the result of statistical manipulations. Our proposed quantification of the impact that a confound may have on functional connectivity can be generalized to global effect estimators other than the GAS. It can be readily applied to other confounds, such as systemic physiological or head movement interferences, in order to quantify their impact on functional connectivity in the resting state. © 2013.

Pre-operative Thresholds for Achieving Meaningful Clinical Improvement after Arthroscopic Treatment of Femoroacetabular Impingement

PubMed Central

Nwachukwu, Benedict U.; Fields, Kara G.; Nawabi, Danyal H.; Kelly, Bryan T.; Ranawat, Anil S.

2016-01-01

Objectives: Knowledge of the thresholds and determinants for successful femoroacetabular impingement (FAI) treatment is evolving. The primary purpose of this study was to define pre-operative outcome score thresholds that can be used to predict patients most likely to achieve meaningful clinically important difference (MCID) after arthroscopic FAI treatment. Secondarily determinants of achieving MCID were evaluated. Methods: A prospective institutional hip arthroscopy registry was reviewed to identify patients with FAI treated with arthroscopic labral surgery, acetabular rim trimming, and femoral osteochondroplasty. The modified Harris Hip Score (mHHS), the Hip Outcome Score (HOS) and the international Hip Outcome Tool (iHOT-33) tools were administered at baseline and at one year post-operatively. MCID was calculated using a distribution-based method. A receiver operating characteristic (ROC) analysis was used to calculate cohort-based threshold values predictive of achieving MCID. Area under the curve (AUC) was used to define predictive ability (strength of association) with AUC >0.7 considered acceptably predictive. Univariate and multivariable analyses were used to analyze demographic, radiographic and intra-operative factors associated with achieving MCID. Results: There were 374 patients (mean + SD age, 32.9 + 10.5) and 56.4% were female. The MCID for mHHS, HOS activities of daily living (HOS-ADL), HOS Sports, and iHOT-33 was 8.2, 8.4,14.5, and 12.0 respectively. ROC analysis (threshold, % achieving MCID, strength of association) for these tools in our population was: mHHS (61.6, 78%, 0.68), HOS-ADL (83.8, 68%, 0.84), HOS-Sports (63.9, 64%, 0.74), and iHOT-33 (54.3, 82%, 0.65). Likelihood for achieving MCID declined above and increased below these thresholds. In univariate analysis female sex, femoral version, lower acetabular outerbridge score and increasing CT sagittal center edge angle (CEA) were predictive of achieving MCID. In multivariable analysis sagittal CEA was the only variable maintaining significance (p = 0.032). Conclusion: We used a large prospective hip arthroscopy database to identify pre-operative patient outcome score thresholds predictive of meaningful post-operative outcome improvement after arthroscopic FAI treatment. This is the largest reported hip arthroscopy cohort to define MCID and the first to do so for iHOT-33. The HOS-ADL may have the best predictive ability for achieving MCID after hip arthroscopy. Patients with relatively high pre-operative ADL, quality of life and functional status appear to have a high chance for achieveing MCID up to our defined thresholds. Hip dysplasia is an important outcome modifier. The findings of this study may be useful for managing preoperative expectation for patients undergoing arthroscopic FAI surgery.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Evaluation of in-line Raman data for end-point determination of a coating process: Comparison of Science-Based Calibration, PLS-regression and univariate data analysis.

PubMed

Barimani, Shirin; Kleinebudde, Peter

2017-10-01

A multivariate analysis method, Science-Based Calibration (SBC), was used for the first time for endpoint determination of a tablet coating process using Raman data. Two types of tablet cores, placebo and caffeine cores, received a coating suspension comprising a polyvinyl alcohol-polyethylene glycol graft-copolymer and titanium dioxide to a maximum coating thickness of 80µm. Raman spectroscopy was used as in-line PAT tool. The spectra were acquired every minute and correlated to the amount of applied aqueous coating suspension. SBC was compared to another well-known multivariate analysis method, Partial Least Squares-regression (PLS) and a simpler approach, Univariate Data Analysis (UVDA). All developed calibration models had coefficient of determination values (R 2 ) higher than 0.99. The coating endpoints could be predicted with root mean square errors (RMSEP) less than 3.1% of the applied coating suspensions. Compared to PLS and UVDA, SBC proved to be an alternative multivariate calibration method with high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Analysis of Forest Foliage Using a Multivariate Mixture Model

NASA Technical Reports Server (NTRS)

Hlavka, C. A.; Peterson, David L.; Johnson, L. F.; Ganapol, B.

1997-01-01

Data with wet chemical measurements and near infrared spectra of ground leaf samples were analyzed to test a multivariate regression technique for estimating component spectra which is based on a linear mixture model for absorbance. The resulting unmixed spectra for carbohydrates, lignin, and protein resemble the spectra of extracted plant starches, cellulose, lignin, and protein. The unmixed protein spectrum has prominent absorption spectra at wavelengths which have been associated with nitrogen bonds.

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

Classification and regression tree analysis vs. multivariable linear and logistic regression methods as statistical tools for studying haemophilia.

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

The importance of extent of choroid plexus cauterization in addition to endoscopic third ventriculostomy for infantile hydrocephalus: a retrospective North American observational study using propensity score-adjusted analysis.

PubMed

Fallah, Aria; Weil, Alexander G; Juraschka, Kyle; Ibrahim, George M; Wang, Anthony C; Crevier, Louis; Tseng, Chi-Hong; Kulkarni, Abhaya V; Ragheb, John; Bhatia, Sanjiv

2017-12-01

OBJECTIVE Combined endoscopic third ventriculostomy (ETC) and choroid plexus cauterization (CPC)-ETV/CPC- is being investigated to increase the rate of shunt independence in infants with hydrocephalus. The degree of CPC necessary to achieve improved rates of shunt independence is currently unknown. METHODS Using data from a single-center, retrospective, observational cohort study involving patients who underwent ETV/CPC for treatment of infantile hydrocephalus, comparative statistical analyses were performed to detect a difference in need for subsequent CSF diversion procedure in patients undergoing partial CPC (describes unilateral CPC or bilateral CPC that only extended from the foramen of Monro [FM] to the atrium on one side) or subtotal CPC (describes CPC extending from the FM to the posterior temporal horn bilaterally) using a rigid neuroendoscope. Propensity scores for extent of CPC were calculated using age and etiology. Propensity scores were used to perform 1) case-matching comparisons and 2) Cox multivariable regression, adjusting for propensity score in the unmatched cohort. Cox multivariable regression adjusting for age and etiology, but not propensity score was also performed as a third statistical technique. RESULTS Eighty-four patients who underwent ETV/CPC had sufficient data to be included in the analysis. Subtotal CPC was performed in 58 patients (69%) and partial CPC in 26 (31%). The ETV/CPC success rates at 6 and 12 months, respectively, were 49% and 41% for patients undergoing subtotal CPC and 35% and 31% for those undergoing partial CPC. Cox multivariate regression in a 48-patient cohort case-matched by propensity score demonstrated no added effect of increased extent of CPC on ETV/CPC survival (HR 0.868, 95% CI 0.422-1.789, p = 0.702). Cox multivariate regression including all patients, with adjustment for propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.845, 95% CI 0.462-1.548, p = 0.586). Cox multivariate regression including all patients, with adjustment for age and etiology, but not propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.908, 95% CI 0.495-1.664, p = 0.755). CONCLUSIONS Using multiple comparative statistical analyses, no difference in need for subsequent CSF diversion procedure was detected between patients in this cohort who underwent partial versus subtotal CPC. Further investigation regarding whether there is truly no difference between partial versus subtotal extent of CPC in larger patient populations and whether further gain in CPC success can be achieved with complete CPC is warranted.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Determining the response of sea level to atmospheric pressure forcing using TOPEX/POSEIDON data

NASA Technical Reports Server (NTRS)

Fu, Lee-Lueng; Pihos, Greg

1994-01-01

The static response of sea level to the forcing of atmospheric pressure, the so-called inverted barometer (IB) effect, is investigated using TOPEX/POSEIDON data. This response, characterized by the rise and fall of sea level to compensate for the change of atmospheric pressure at a rate of -1 cm/mbar, is not associated with any ocean currents and hence is normally treated as an error to be removed from sea level observation. Linear regression and spectral transfer function analyses are applied to sea level and pressure to examine the validity of the IB effect. In regions outside the tropics, the regression coefficient is found to be consistently close to the theoretical value except for the regions of western boundary currents, where the mesoscale variability interferes with the IB effect. The spectral transfer function shows near IB response at periods of 30 degrees is -0.84 +/- 0.29 cm/mbar (1 standard deviation). The deviation from = 1 cm /mbar is shown to be caused primarily by the effect of wind forcing on sea level, based on multivariate linear regression model involving both pressure and wind forcing. The regression coefficient for pressure resulting from the multivariate analysis is -0.96 +/- 0.32 cm/mbar. In the tropics the multivariate analysis fails because sea level in the tropics is primarily responding to remote wind forcing. However, after removing from the data the wind-forced sea level estimated by a dynamic model of the tropical Pacific, the pressure regression coefficient improves from -1.22 +/- 0.69 cm/mbar to -0.99 +/- 0.46 cm/mbar, clearly revealing an IB response. The result of the study suggests that with a proper removal of the effect of wind forcing the IB effect is valid in most of the open ocean at periods longer than 20 days and spatial scales larger than 500 km.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression.

PubMed

Delwiche, Stephen R; Reeves, James B

2010-01-01

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly smoothing operations or derivatives. While such operations are often useful in reducing the number of latent variables of the actual decomposition and lowering residual error, they also run the risk of misleading the practitioner into accepting calibration equations that are poorly adapted to samples outside of the calibration. The current study developed a graphical method to examine this effect on partial least squares (PLS) regression calibrations of near-infrared (NIR) reflection spectra of ground wheat meal with two analytes, protein content and sodium dodecyl sulfate sedimentation (SDS) volume (an indicator of the quantity of the gluten proteins that contribute to strong doughs). These two properties were chosen because of their differing abilities to be modeled by NIR spectroscopy: excellent for protein content, fair for SDS sedimentation volume. To further demonstrate the potential pitfalls of preprocessing, an artificial component, a randomly generated value, was included in PLS regression trials. Savitzky-Golay (digital filter) smoothing, first-derivative, and second-derivative preprocess functions (5 to 25 centrally symmetric convolution points, derived from quadratic polynomials) were applied to PLS calibrations of 1 to 15 factors. The results demonstrated the danger of an over reliance on preprocessing when (1) the number of samples used in a multivariate calibration is low (<50), (2) the spectral response of the analyte is weak, and (3) the goodness of the calibration is based on the coefficient of determination (R(2)) rather than a term based on residual error. The graphical method has application to the evaluation of other preprocess functions and various types of spectroscopy data.

An integrated evaluation of some faecal indicator bacteria (FIB) and chemical markers as potential tools for monitoring sewage contamination in subtropical estuaries.

PubMed

Cabral, Ana Caroline; Stark, Jonathan S; Kolm, Hedda E; Martins, César C

2018-04-01

Sewage input and the relationship between chemical markers (linear alkylbenzenes and coprostanol) and fecal indicator bacteria (FIB, Escherichia coli and enterococci), were evaluated in order to establish thresholds values for chemical markers in suspended particulate matter (SPM) as indicators of sewage contamination in two subtropical estuaries in South Atlantic Brazil. Both chemical markers presented no linear relationship with FIB due to high spatial microbiological variability, however, microbiological water quality was related to coprostanol values when analyzed by logistic regression, indicating that linear models may not be the best representation of the relationship between both classes of indicators. Logistic regression was performed with all data and separately for two sampling seasons, using 800 and 100 MPN 100 mL -1 of E. coli and enterococci, respectively, as the microbiological limits of sewage contamination. Threshold values of coprostanol varied depending on the FIB and season, ranging between 1.00 and 2.23 μg g -1 SPM. The range of threshold values of coprostanol for SPM are relatively higher and more variable than those suggested in literature for sediments (0.10-0.50 μg g -1 ), probably due to higher concentration of coprostanol in SPM than in sediment. Temperature may affect the relationship between microbiological indicators and coprostanol, since the threshold value of coprostanol found here was similar to tropical areas, but lower than those found during winter in temperate areas, reinforcing the idea that threshold values should be calibrated for different climatic conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Effective Dynamic Ranges for Glaucomatous Visual Field Progression With Standard Automated Perimetry and Stimulus Sizes III and V

PubMed Central

Zamba, Gideon K. D.; Artes, Paul H.

2018-01-01

Purpose It has been shown that threshold estimates below approximately 20 dB have little effect on the ability to detect visual field progression in glaucoma. We aimed to compare stimulus size V to stimulus size III, in areas of visual damage, to confirm these findings by using (1) a different dataset, (2) different techniques of progression analysis, and (3) an analysis to evaluate the effect of censoring on mean deviation (MD). Methods In the Iowa Variability in Perimetry Study, 120 glaucoma subjects were tested every 6 months for 4 years with size III SITA Standard and size V Full Threshold. Progression was determined with three complementary techniques: pointwise linear regression (PLR), permutation of PLR, and linear regression of the MD index. All analyses were repeated on “censored'' datasets in which threshold estimates below a given criterion value were set to equal the criterion value. Results Our analyses confirmed previous observations that threshold estimates below 20 dB contribute much less to visual field progression than estimates above this range. These findings were broadly similar with stimulus sizes III and V. Conclusions Censoring of threshold values < 20 dB has relatively little impact on the rates of visual field progression in patients with mild to moderate glaucoma. Size V, which has lower retest variability, performs at least as well as size III for longitudinal glaucoma progression analysis and appears to have a larger useful dynamic range owing to the upper sensitivity limit being higher. PMID:29356822

Total body weight loss of ≥ 10 % is associated with improved hepatic fibrosis in patients with nonalcoholic steatohepatitis.

PubMed

Glass, Lisa M; Dickson, Rolland C; Anderson, Joseph C; Suriawinata, Arief A; Putra, Juan; Berk, Brian S; Toor, Arifa

2015-04-01

Given the rising epidemics of obesity and metabolic syndrome, nonalcoholic steatohepatitis (NASH) is now the most common cause of liver disease in the developed world. Effective treatment for NASH, either to reverse or prevent the progression of hepatic fibrosis, is currently lacking. To define the predictors associated with improved hepatic fibrosis in NASH patients undergoing serial liver biopsies at prolonged biopsy interval. This is a cohort study of 45 NASH patients undergoing serial liver biopsies for clinical monitoring in a tertiary care setting. Biopsies were scored using the NASH Clinical Research Network guidelines. Fibrosis regression was defined as improvement in fibrosis score ≥1 stage. Univariate analysis utilized Fisher's exact or Student's t test. Multivariate regression models determined independent predictors for regression of fibrosis. Forty-five NASH patients with biopsies collected at a mean interval of 4.6 years (±1.4) were included. The mean initial fibrosis stage was 1.96, two patients had cirrhosis and 12 patients (26.7 %) underwent bariatric surgery. There was a significantly higher rate of fibrosis regression among patients who lost ≥10 % total body weight (TBW) (63.2 vs. 9.1 %; p = 0.001) and who underwent bariatric surgery (47.4 vs. 4.5 %; p = 0.003). Factors such as age, gender, glucose intolerance, elevated ferritin, and A1AT heterozygosity did not influence fibrosis regression. On multivariate analysis, only weight loss of ≥10 % TBW predicted fibrosis regression [OR 8.14 (CI 1.08-61.17)]. Results indicate that regression of fibrosis in NASH is possible, even in advanced stages. Weight loss of ≥10 % TBW predicts fibrosis regression.

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Molecular Classification Substitutes for the Prognostic Variables Stage, Age, and MYCN Status in Neuroblastoma Risk Assessment.

PubMed

Rosswog, Carolina; Schmidt, Rene; Oberthuer, André; Juraeva, Dilafruz; Brors, Benedikt; Engesser, Anne; Kahlert, Yvonne; Volland, Ruth; Bartenhagen, Christoph; Simon, Thorsten; Berthold, Frank; Hero, Barbara; Faldum, Andreas; Fischer, Matthias

2017-12-01

Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis. We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Predicting hearing thresholds and occupational hearing loss with multiple-frequency auditory steady-state responses.

PubMed

Hsu, Ruey-Fen; Ho, Chi-Kung; Lu, Sheng-Nan; Chen, Shun-Sheng

2010-10-01

An objective investigation is needed to verify the existence and severity of hearing impairments resulting from work-related, noise-induced hearing loss in arbitration of medicolegal aspects. We investigated the accuracy of multiple-frequency auditory steady-state responses (Mf-ASSRs) between subjects with sensorineural hearing loss (SNHL) with and without occupational noise exposure. Cross-sectional study. Tertiary referral medical centre. Pure-tone audiometry and Mf-ASSRs were recorded in 88 subjects (34 patients had occupational noise-induced hearing loss [NIHL], 36 patients had SNHL without noise exposure, and 18 volunteers were normal controls). Inter- and intragroup comparisons were made. A predicting equation was derived using multiple linear regression analysis. ASSRs and pure-tone thresholds (PTTs) showed a strong correlation for all subjects (r = .77 ≈ .94). The relationship is demonstrated by the equationThe differences between the ASSR and PTT were significantly higher for the NIHL group than for the subjects with non-noise-induced SNHL (p < .001). Mf-ASSR is a promising tool for objectively evaluating hearing thresholds. Predictive value may be lower in subjects with occupational hearing loss. Regardless of carrier frequencies, the severity of hearing loss affects the steady-state response. Moreover, the ASSR may assist in detecting noise-induced injury of the auditory pathway. A multiple linear regression equation to accurately predict thresholds was shown that takes into consideration all effect factors.

Major controlling factors and prediction models for arsenic uptake from soil to wheat plants.

PubMed

Dai, Yunchao; Lv, Jialong; Liu, Ke; Zhao, Xiaoyan; Cao, Yingfei

2016-08-01

The application of current Chinese agriculture soil quality standards fails to evaluate the land utilization functions appropriately due to the diversity of soil properties and plant species. Therefore, the standards should be amended. A greenhouse experiment was conducted to investigate arsenic (As) enrichment in various soils from 18 Chinese provinces in parallel with As transfer to 8 wheat varieties. The goal of the study was to build and calibrate soil-wheat threshold models to forecast the As threshold of wheat soils. In Shaanxi soils, Wanmai and Jimai were the most sensitive and insensitive wheat varieties, respectively; and in Jiangxi soils, Zhengmai and Xumai were the most sensitive and insensitive wheat varieties, respectively. Relationships between soil properties and the bioconcentration factor (BCF) were built based on stepwise multiple linear regressions. Soil pH was the best predictor of BCF, and after normalizing the regression equation (Log BCF=0.2054 pH- 3.2055, R(2)=0.8474, n=14, p<0.001), we obtained a calibrated model. Using the calibrated model, a continuous soil-wheat threshold equation (HC5=10((-0.2054 pH+2.9935))+9.2) was obtained for the species-sensitive distribution curve, which was built on Chinese food safety standards. The threshold equation is a helpful tool that can be applied to estimate As uptake from soil to wheat. Copyright © 2016 Elsevier Inc. All rights reserved.

Acute effects of dynamic exercises on the relationship between the motor unit firing rate and the recruitment threshold.

PubMed

Ye, Xin; Beck, Travis W; DeFreitas, Jason M; Wages, Nathan P

2015-04-01

The aim of this study was to compare the acute effects of concentric versus eccentric exercise on motor control strategies. Fifteen men performed six sets of 10 repetitions of maximal concentric exercises or eccentric isokinetic exercises with their dominant elbow flexors on separate experimental visits. Before and after the exercise, maximal strength testing and submaximal trapezoid isometric contractions (40% of the maximal force) were performed. Both exercise conditions caused significant strength loss in the elbow flexors, but the loss was greater following the eccentric exercise (t=2.401, P=.031). The surface electromyographic signals obtained from the submaximal trapezoid isometric contractions were decomposed into individual motor unit action potential trains. For each submaximal trapezoid isometric contraction, the relationship between the average motor unit firing rate and the recruitment threshold was examined using linear regression analysis. In contrast to the concentric exercise, which did not cause significant changes in the mean linear slope coefficient and y-intercept of the linear regression line, the eccentric exercise resulted in a lower mean linear slope and an increased mean y-intercept, thereby indicating that increasing the firing rates of low-threshold motor units may be more important than recruiting high-threshold motor units to compensate for eccentric exercise-induced strength loss. Copyright © 2014 Elsevier B.V. All rights reserved.

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Simultaneous determination of estrogens (ethinylestradiol and norgestimate) concentrations in human and bovine serum albumin by use of fluorescence spectroscopy and multivariate regression analysis.

PubMed

Hordge, LaQuana N; McDaniel, Kiara L; Jones, Derick D; Fakayode, Sayo O

2016-05-15

The endocrine disruption property of estrogens necessitates the immediate need for effective monitoring and development of analytical protocols for their analyses in biological and human specimens. This study explores the first combined utility of a steady-state fluorescence spectroscopy and multivariate partial-least-square (PLS) regression analysis for the simultaneous determination of two estrogens (17α-ethinylestradiol (EE) and norgestimate (NOR)) concentrations in bovine serum albumin (BSA) and human serum albumin (HSA) samples. The influence of EE and NOR concentrations and temperature on the emission spectra of EE-HSA EE-BSA, NOR-HSA, and NOR-BSA complexes was also investigated. The binding of EE with HSA and BSA resulted in increase in emission characteristics of HSA and BSA and a significant blue spectra shift. In contrast, the interaction of NOR with HSA and BSA quenched the emission characteristics of HSA and BSA. The observed emission spectral shifts preclude the effective use of traditional univariate regression analysis of fluorescent data for the determination of EE and NOR concentrations in HSA and BSA samples. Multivariate partial-least-squares (PLS) regression analysis was utilized to correlate the changes in emission spectra with EE and NOR concentrations in HSA and BSA samples. The figures-of-merit of the developed PLS regression models were excellent, with limits of detection as low as 1.6×10(-8) M for EE and 2.4×10(-7) M for NOR and good linearity (R(2)>0.994985). The PLS models correctly predicted EE and NOR concentrations in independent validation HSA and BSA samples with a root-mean-square-percent-relative-error (RMS%RE) of less than 6.0% at physiological condition. On the contrary, the use of univariate regression resulted in poor predictions of EE and NOR in HSA and BSA samples, with RMS%RE larger than 40% at physiological conditions. High accuracy, low sensitivity, simplicity, low-cost with no prior analyte extraction or separation required makes this method promising, compelling, and attractive alternative for the rapid determination of estrogen concentrations in biomedical and biological specimens, pharmaceuticals, or environmental samples. Published by Elsevier B.V.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Negative Events in Childhood Predict Trajectories of Internalizing Symptoms Up to Young Adulthood: An 18-Year Longitudinal Study

PubMed Central

Melchior, Maria; Touchette, Évelyne; Prokofyeva, Elena; Chollet, Aude; Fombonne, Eric; Elidemir, Gulizar; Galéra, Cédric

2014-01-01

Background Common negative events can precipitate the onset of internalizing symptoms. We studied whether their occurrence in childhood is associated with mental health trajectories over the course of development. Methods Using data from the TEMPO study, a French community-based cohort study of youths, we studied the association between negative events in 1991 (when participants were aged 4–16 years) and internalizing symptoms, assessed by the ASEBA family of instruments in 1991, 1999, and 2009 (n = 1503). Participants' trajectories of internalizing symptoms were estimated with semi-parametric regression methods (PROC TRAJ). Data were analyzed using multinomial regression models controlled for participants' sex, age, parental family status, socio-economic position, and parental history of depression. Results Negative childhood events were associated with an increased likelihood of concurrent internalizing symptoms which sometimes persisted into adulthood (multivariate ORs associated with > = 3 negative events respectively: high and decreasing internalizing symptoms: 5.54, 95% CI: 3.20–9.58; persistently high internalizing symptoms: 8.94, 95% CI: 2.82–28.31). Specific negative events most strongly associated with youths' persistent internalizing symptoms included: school difficulties (multivariate OR: 5.31, 95% CI: 2.24–12.59), parental stress (multivariate OR: 4.69, 95% CI: 2.02–10.87), serious illness/health problems (multivariate OR: 4.13, 95% CI: 1.76–9.70), and social isolation (multivariate OR: 2.24, 95% CI: 1.00–5.08). Conclusions Common negative events can contribute to the onset of children's lasting psychological difficulties. PMID:25485875

qFeature

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Definition of prepartum hyperketonemia in dairy goats.

PubMed

Doré, V; Dubuc, J; Bélanger, A M; Buczinski, S

2015-07-01

A prospective cohort study was conducted on 1,081 dairy goats from 10 commercial herds in Québec (Canada) to define prepartum hyperketonemia based on optimal blood β-hydroxybutyrate acid threshold values for the early prediction of pregnancy toxemia (PT) and mortality in late-gestation dairy goats. All pregnant goats had blood sampled weekly during the last 5wk of pregnancy. The blood was analyzed directly on the farm for β-hydroxybutyrate acid quantification using a Precision Xtra meter (Abbott Diabetes Care, Saint-Laurent, QC, Canada). Body condition scores on the lumbar region and sternum were noted. Each goat was classified as being at low (n=973) or high risk (n=108) of having PT by producers based on a standardized definition. The optimal threshold for predicting a PT diagnosis or mortality for each week before kidding was determined based on the highest sum of sensitivity and specificity. The association between hyperketonemia and subsequent PT was tested using a multivariable logistic regression model considering hyperketonemia at wk 4 prepartum, litter size, and body condition score at wk 4 prepartum as covariates, and herd and parturition cohort as random effects. The association between mortality and hyperketonemia was also tested using a logistic regression model accounting for the presence or absence of treatment during the last month of pregnancy. The hyperketonemia definition based on PT varied between ≥0.4 and ≥0.9mmol/L during the last 5wk prepartum. Goats affected by hyperketonemia at wk 4 prepartum and with a large litter size (≥3 fetuses) had 2.1 and 40.5 times the odds, respectively, of subsequent PT than other goats. Hyperketonemia definitions based on mortality varied between ≥0.6 and ≥1.4mmol/L during the last 4wk prepartum, and was ≥1.7mmol/L during the first week postpartum. Goats affected by hyperketonemia and treated by producers had 3.4 and 11.8 times the odds, respectively, of subsequent mortality than did other goats. These results showed that prepartum hyperketonemia could be defined in dairy goats using subsequent risks of PT or mortality during the last month of pregnancy. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Triatoma dimidiata Infestation in Chagas Disease Endemic Regions of Guatemala: Comparison of Random and Targeted Cross-Sectional Surveys

PubMed Central

King, Raymond J.; Cordon-Rosales, Celia; Cox, Jonathan; Kitron, Uriel D.

2011-01-01

Background Guatemala is presently engaged in the Central America Initiative to interrupt Chagas disease transmission by reducing intradomiciliary prevalence of Triatoma dimidiata, using targeted cross-sectional surveys to direct control measures to villages exceeding the 5% control threshold. The use of targeted surveys to guide disease control programs has not been evaluated. Here, we compare the findings from the targeted surveys to concurrent random cross-sectional surveys in two primary foci of Chagas disease transmission in central and southeastern Guatemala. Methodology/Principal Findings Survey prevalences of T. dimidiata intradomiciliary infestation by village and region were compared. Univariate logistic regression was used to assess the use of risk factors to target surveys and to evaluate indicators associated with village level intradomiciliary prevalences >5% by survey and region. Multivariate logistic regression models were developed to assess the ability of random and targeted surveys to target villages with intradomiciliary prevalence exceeding the control threshold within each region. Regional prevalences did not vary by survey; however, village prevalences were significantly greater in random surveys in central (13.0% versus 8.7%) and southeastern (22.7% versus 6.9%) Guatemala. The number of significant risk factors detected did not vary by survey in central Guatemala but differed considerably in the southeast with a greater number of significant risk factors in the random survey (e.g. land surface temperature, relative humidity, cropland, grassland, tile flooring, and stick and mud and palm and straw walls). Differences in the direction of risk factor associations were observed between regions in both survey types. The overall discriminative capacity was significantly greater in the random surveys in central and southeastern Guatemala, with an area under the receiver-operator curve (AUC) of 0.84 in the random surveys and approximately 0.64 in the targeted surveys in both regions. Sensitivity did not differ between surveys, but the positive predictive value was significantly greater in the random surveys. Conclusions/Significance Surprisingly, targeted surveys were not more effective at determining T. dimidiata prevalence or at directing control to high risk villages in comparison to random surveys. We recommend that random surveys should be selected over targeted surveys whenever possible, particularly when the focus is on directing disease control and elimination and when risk factor association has not been evaluated for all regions under investigation. PMID:21532742

Blood product transfusion in emergency department patients: a case-control study of practice patterns and impact on outcome.

PubMed

Beyer, Alexander; Rees, Ryan; Palmer, Christopher; Wessman, Brian T; Fuller, Brian M

2017-12-01

Blood product transfusion occurs in a significant percentage of intensive care unit (ICU) patients. Pulmonary complications, such as acute respiratory distress syndrome (ARDS), occurring in the setting of transfusion, are associated with increased morbidity and mortality. Contrary to the ICU setting, there is little evidence describing the epidemiology of transfusion in the emergency department (ED) or its potential impact on outcome. The objectives of this study were to: (1) characterize transfusion practices in the ED with respect to patient characteristics and pre-transfusion laboratory values; and (2) investigate the effect of ED blood product transfusion on the incidence of pulmonary complications after admission. We hypothesized that blood product transfusion would increase the event rate for pulmonary complications, and have a negative impact on other clinically significant outcomes. This was a retrospective case-control study with one-one matching of 204 transfused ED patients to 204 non-transfused controls. The primary outcome was a composite pulmonary outcome that included: acute respiratory failure, new need for ICU admission, and ARDS. Multivariable logistic regression was used to evaluate the primary outcome as a function of transfusion. One-hundred twenty four (60.8%) patients were transfused packed red blood cells (PRBC) in the ED. The mean pre-transfusion hemoglobin level was 8.5 g/dl. There were 73 patients with a hemoglobin value ≥10 g/dl; 19 (26.0%) received a PRBC transfusion. A total of 54 (26.5%) patients were transfused platelets. The main indications were thrombocytopenia (27.8%) and neurologic injury (24.1%). Ten patients had a platelet level <10,000 (guideline recommended threshold for transfusion to prevent spontaneous hemorrhage). The mean platelet count for neurologic injury patients was 197,000 prior to transfusion. The primary outcome occurred in 26 control patients (12.7%), as compared with 28 cases (13.7%). In multivariable logistic regression analysis, ED transfusion was not associated with an increased odds of primary outcome [adjusted OR 0.91 (0.48-1.72), P = 0.77]. The mortality rate was 10.8% in the cases and 8.8% in the controls, P = 0.51. A significant percentage of ED blood product transfusions are discordant with guideline recommendations. However, there was no association with ED transfusion and worse clinical outcome.

Optimal tumor shrinkage predicts long-term outcome in advanced nonsmall cell lung cancer (NSCLC) treated with target therapy: Result from 3 clinical trials of advanced NSCLC by 1 institution.

PubMed

He, Xiaobo; Zhang, Yang; Ma, Yuxiang; Zhou, Ting; Zhang, Jianwei; Hong, Shaodong; Sheng, Jin; Zhang, Zhonghan; Yang, Yunpeng; Huang, Yan; Zhang, Li; Zhao, Hongyun

2016-08-01

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are used as standard therapies for advanced nonsmall cell lung cancer (NSCLC) patients with EGFR mutation positive. Because these targeted therapies could cause tumor necrosis and shrinkage, the purpose of the study is to search for a value of optimal tumor shrinkage as an appropriate indicator of outcome for advanced NSCLC.A total of 88 NSCLC enrollees of 3 clinical trials (IRESSA registration clinical trial, TRUST study and ZD6474 study), who received Gefitinib (250 mg, QD), Erlotinib (150 mg, QD), and ZD6474 (100 mg, QD), respectively, during December 2003 and October 2007, were retrospectively analyzed. The response evaluation criteria in solid tumors (RECIST) were used to identify responders, who had complete response (CR) or partial responses (PR) and nonresponders who had stable disease (SD) or progressive disease (PD). Receiver operating characteristics (ROC) analysis was used to find the optimal tumor shrinkage as an indicator for tumor therapeutic outcome. Univariate and multivariate Cox regression analyses were performed to compare the progression-free survival (PFS) and overall survival (OS) between responders and nonresponders stratified based on radiologic criteria.Among the 88 NSCLC patients, 26 were responders and 62 were nonresponders based on RECIST 1.0. ROC indicated that 8.32% tumor diameter shrinkage in the sum of the longest tumor diameter (SLD) was the cutoff point of tumor shrinkage outcomes, resulting in 46 responders (≤8.32%) and 42 nonresponders (≥8.32%). Univariate and multivariate Cox regression analyses indicated that (1) the responders (≤8.32%) and nonresponders (≥ -8.32%) were significantly different in median PFS (13.40 vs 1.17 months, P < 0.001) and OS (19.80 vs 7.90 months, P < 0.001) and (2) -8.32% in SLD could be used as the optimal threshold for PFS (hazard ratio [HR], 8.11, 95% CI, 3.75 to 17.51, P < 0.001) and OS (HR, 2.36, 95% CI, 1.41 to 3.96, P = 0.001).However, 8.32% tumor diameter shrinkage is validated as a reliable outcome predictor of advanced NSCLC patients receiving EGFR-TKIs therapies and may provide a practical measure to guide therapeutic decisions.

Meeting the WHO 90% target: antiretroviral treatment efficacy in Poland is associated with baseline clinical patient characteristics

PubMed Central

Parczewski, Milosz; Siwak, Ewa; Leszczyszyn-Pynka, Magdalena; Cielniak, Iwona; Burkacka, Ewa; Pulik, Piotr; Witor, Adam; Muller, Karolina; Zasik, Ewelina; Grzeszczuk, Anna; Jankowska, Maria; Lemańska, Małgorzata; Olczak, Anita; Grąbczewska, Edyta; Szymczak, Aleksandra; Gąsiorowski, Jacek; Szetela, Bartosz; Bociąga-Jasik, Monika; Skwara, Paweł; Witak-Jędra, Magdalena; Jabłonowska, Elżbieta; Wójcik-Cichy, Kamila; Kamerys, Juliusz; Janczarek, Małgorzata; Krankowska, Dagny; Mikuła, Tomasz; Kozieł, Katarzyna; Bielec, Dariusz; Stempkowska, Justyna; Kocbach, Aleksandra; Błudzin, Wiesława; Horban, Andrzej

2017-01-01

Abstract Introduction: Modern combined antiretroviral therapies (cART) allow to effectively suppress HIV-1 viral load, with the 90% virologic success rate, meeting the WHO target in most clinical settings. The aim of this study was to analyse antiretroviral treatment efficacy in Poland and to identify variables associated with virologic suppression. Methods: Cross-sectional data on 5152 (56.92% of the countrywide treated at the time-point of analysis) patients on cART for more than six months with at least one HIV-RNA measurement in 2016 were collected from 14 Polish centres. Patients’ characteristics and treatment type-based outcomes were analysed for the virologic suppression thresholds of <50 and <200 HIV-RNA copies/ml. CART was categorized into two nucleos(t)ide (2NRTI) plus non-nucleoside reverse transcriptase (NNRTI) inhibitors, 2NRTI plus protease (PI) inhibitor, 2NRTI plus integrase (InI) inhibitor, nucleos(t)ide sparing PI/r+InI and three drug class regimens. For statistics Chi-square and U-Mann Whitney tests and adjusted multivariate logistic regression models were used. Results: Virologic suppression rates of <50 copies/mL were observed in 4672 (90.68%) and <200 copies/mL in 4934 (95.77%) individuals. In univariate analyses, for the suppression threshold <50 copies/mL higher efficacy was noted for 2NRTI+NNRTI-based combinations (94.73%) compared to 2NRTI+PI (89.93%), 2NRTI+InI (90.61%), nucleos(t)ide sparing PI/r+InI (82.02%) and three drug class regimens (74.49%) (p < 0.0001), with less pronounced but significant differences for the threshold of 200 copies/mL [2NRTI+NNRTI-97.61%, 2NRTI+PI-95.27%, 2NRTI+InI-96.61%, PI/r+InI- 95.51% and 86.22% for three drug class cART) (p < 0.0001). However, in multivariate model, virologic efficacy for viral load <50 copies/mL was similar across treatment groups with significant influence by history of AIDS [OR:1.48 (95%CI:1.01–2.17) if AIDS diagnosed, p = 0.046], viral load < 5 log copies/mL at care entry [OR:1.47 (95%CI:1.08–2.01), p = 0.016], baseline lymphocyte CD4 count ≥200 cells/µL [OR:1.72 (95%CI:1.04–2.78), p = 0.034] and negative HCV serology [OR:1.97 (95%CI:1.29–2.94), p = 0.002]. For viral load threshold <200 copies/mL higher likelihood of virologic success was only associated with baseline lymphocyte CD4 count ≥200 cells/µL [OR:2.08 (95%CI:1.01–4.35), p = 0.049] and negative HCV status [OR:2.84 (95%CI:1.52–5.26), p = 0.001]. Conclusions: Proportion of virologically suppressed patients is in line with WHO treatment target confirming successful application of antiretroviral treatment strategy in Poland. Virological suppression rates depend on baseline patient characteristics, which should guide individualized antiretroviral tre0atment decisions. PMID:28715160

A mismatch between population health literacy and the complexity of health information: an observational study.

PubMed

Rowlands, Gillian; Protheroe, Joanne; Winkley, John; Richardson, Marty; Seed, Paul T; Rudd, Rima

2015-06-01

Low health literacy is associated with poorer health and higher mortality. Complex health materials are a barrier to health. To assess the literacy and numeracy skills required to understand and use commonly used English health information materials, and to describe population skills in relation to these. An English observational study comparing health materials with national working-age population skills. Health materials were sampled using a health literacy framework. Competency thresholds to understand and use the materials were identified. The proportion of the population above and below these thresholds, and the sociodemographic variables associated with a greater risk of being below the thresholds, were described. Sixty-four health materials were sampled. Two competency thresholds were identified: text (literacy) only, and text + numeracy; 2515/5795 participants (43%) were below the text-only threshold, while 2905/4767 (61%) were below the text + numeracy threshold. Univariable analyses of social determinants of health showed that those groups more at risk of socioeconomic deprivation had higher odds of being below the health literacy competency threshold than those at lower risk of deprivation. Multivariable analysis resulted in some variables becoming non-significant or reduced in effect. Levels of low health literacy mirror those found in other industrialised countries, with a mismatch between the complexity of health materials and the skills of the English adult working-age population. Those most in need of health information have the least access to it. Efficacious strategies are building population skills, improving health professionals' communication, and improving written health information. © British Journal of General Practice 2015.

Quantification and determinants of the amount of respiratory syncytial virus (RSV) shed using real time PCR data from a longitudinal household study.

PubMed

Wathuo, Miriam; Medley, Graham F; Nokes, D James; Munywoki, Patrick K

2016-12-14

Background A better understanding of respiratory syncytial virus (RSV) epidemiology requires realistic estimates of RSV shedding patterns, quantities shed, and identification of the related underlying factors. Methods RSV infection data arise from a cohort study of 47 households with 493 occupants, in coastal Kenya, during the 2009/2010 RSV season. Nasopharyngeal swabs were taken every 3 to 4 days and screened for RSV using a real time polymerase chain reaction (PCR) assay. The amount of virus shed was quantified by calculating the 'area under the curve' using the trapezoidal rule applied to rescaled PCR cycle threshold output. Multivariable linear regression was used to identify correlates of amount of virus shed. Results The median quantity of virus shed per infection episode was 29.4 (95% CI: 15.2, 54.2) log 10 ribonucleic acid (RNA) copies. Young age (<1 year), presence of upper respiratory symptoms, intra-household acquisition of infection, an individual's first infection episode in the RSV season, and having a co-infection of RSV group A and B were associated with increased amount of virus shed. Conclusions The findings provide insight into which groups of individuals have higher potential for transmission, information which may be useful in designing RSV prevention strategies.

Associations between ACE-Inhibitors, Angiotensin Receptor Blockers, and Lean Body Mass in Community Dwelling Older Women.

PubMed

Bea, Jennifer W; Wassertheil-Smoller, Sylvia; Wertheim, Betsy C; Klimentidis, Yann; Chen, Zhao; Zaslavsky, Oleg; Manini, Todd M; Womack, Catherine R; Kroenke, Candyce H; LaCroix, Andrea Z; Thomson, Cynthia A

2018-01-01

Studies suggest that ACE-inhibitors (ACE-I) and angiotensin receptor blockers (ARBs) may preserve skeletal muscle with aging. We evaluated longitudinal differences in lean body mass (LBM) among women diagnosed with hypertension and classified as ACE-I/ARB users and nonusers among Women's Health Initiative participants that received dual energy X-ray absorptiometry scans to estimate body composition ( n =10,635) at baseline and at years 3 and 6 of follow-up. Of those, 2642 were treated for hypertension at baseline. Multivariate linear regression models, adjusted for relevant demographics, behaviors, and medications, assessed ACE-I/ARB use/nonuse and LBM associations at baseline, as well as change in LBM over 3 and 6 years. Although BMI did not differ by ACE-I/ARB use, LBM (%) was significantly higher in ACE-I/ARB users versus nonusers at baseline (52.2% versus 51.3%, resp., p =0.001). There was no association between ACE-I/ARB usage and change in LBM over time. Reasons for higher LBM with ACE-I/ARB use cross sectionally, but not longitundinally, are unclear and may reflect a threshold effect of these medications on LBM that is attenuated over time. Nevertheless, ACE-I/ARB use does not appear to negatively impact LBM in the long term.

Determinants of catastrophic health expenditure in Nigeria.

PubMed

Aregbeshola, Bolaji Samson; Khan, Samina Mohsin

2018-05-01

Catastrophic health expenditure is a measure of financial risk protection and it is often incurred by households who have to pay out of pocket for health care services that are not affordable. The study assessed the determinants of catastrophic health expenditure among households in Nigeria. Secondary data from the Harmonized Nigeria Living Standard Survey (HNLSS) of 2009/10 was utilized to assess factors associated with catastrophic health expenditure in Nigeria. Household and individual characteristics associated with catastrophic health expenditure were determined using bivariate analysis and multivariate logistic regression. Results showed that irrespective of the threshold for the two concepts of total household expenditure and non-food expenditure, having household members aged between 6 and 14 years, having household members aged between 15 and 24 years, having household members aged between 25 and 54 years, having no education, having primary education, having secondary education, lack of health insurance coverage, visiting a private health facility, households living in north central zone, households living in north east zone and having household members with non-chronic illnesses were factors that increase the risk of incurring catastrophic health expenditure among households. Policy-makers and political actors need to design equitable health financing policies that will increase financial risk protection for people in both the formal and informal sectors of the economy.

Physical activity, inflammatory biomarkers in gingival crevicular fluid and periodontitis.

PubMed

Sanders, Anne E; Slade, Gary D; Fitzsimmons, Tracy R; Bartold, Peter Mark

2009-05-01

To examine the associations of physical activity with interleukin 1-beta (IL-1beta), C-reactive protein (CRP) and periodontitis and to investigate whether any relationship between physical activity and inflammatory mediators differs between periodontitis cases and non-cases. In this population-based case control study of Australians aged 18+ years, dentists conducted oral epidemiologic examinations identifying cases with moderate or severe periodontitis and periodontally healthy controls. Gingival crevicular fluid samples collected during examinations were analysed for inflammatory biomarkers. Subject-completed questionnaires assessed leisure-time physical activity. Exposure odds ratios (ORs) were estimated in multivariable logistic regression models adjusting for periodontitis risk indicators. Of 751 subjects (359 cases, 392 controls), those meeting a prescribed threshold for leisure-time physical activity had lower adjusted odds of elevated IL-1beta: OR=0.69, (95% CI=0.50-0.94) and detectable CRP: OR=0.70 (0.50-0.98) than less active adults. Physical activity was not associated with periodontitis: OR=1.14 (0.80-1.62). Periodontitis modified the association between levels of physical activity and detectable CRP. Increasing quartiles of physically activity were associated with decreasing probability of detectable CRP, but the effect was limited to periodontitis cases and was not apparent among non-cases. Leisure-time physical activity may protect against an excessive inflammatory response in periodontitis.

Comparison of three lifecourse models of poverty in predicting cardiovascular disease risk in youth.

PubMed

Kakinami, Lisa; Séguin, Louise; Lambert, Marie; Gauvin, Lise; Nikiema, Béatrice; Paradis, Gilles

2013-08-01

Childhood poverty heightens the risk of adulthood cardiovascular disease (CVD), but the underlying pathways are poorly understood. Three lifecourse models have been proposed but have never been tested among youth. We assessed the longitudinal association of childhood poverty with CVD risk factors in 10-year-old youth according to the timing, accumulation, and mobility models. The Québec Longitudinal Study of Child Development birth cohort was established in 1998 (n = 2120). Poverty was defined as annual income below the low-income thresholds defined by Statistics Canada. Multiple imputation was used for missing data. Multivariable linear regression models adjusted for gender, pubertal stage, parental education, maternal age, whether the household was a single parent household, whether the child was overweight or obese, the child's physical activity in the past week, and family history. Approximately 40% experienced poverty at least once, 16% throughout childhood, and 25% intermittently. Poverty was associated with significantly elevated triglycerides and insulin according to the timing and accumulation models, although the timing model was superior for predicting insulin and the accumulation model was superior for predicting triglycerides. Early and prolonged exposure to poverty significantly increases CVD risk among 10-year-old youth. Copyright © 2013 Elsevier Inc. All rights reserved.

Review-of-systems questionnaire as a predictive tool for psychogenic nonepileptic seizures.

PubMed

Robles, Liliana; Chiang, Sharon; Haneef, Zulfi

2015-04-01

Patients with refractory epilepsy undergo video-electroencephalography for seizure characterization, among whom approximately 10-30% will be discharged with the diagnosis of psychogenic nonepileptic seizures (PNESs). Clinical PNES predictors have been described but in general are not sensitive or specific. We evaluated whether multiple complaints in a routine review-of-system (ROS) questionnaire could serve as a sensitive and specific marker of PNESs. We performed a retrospective analysis of a standardized ROS questionnaire completed by patients with definite PNESs and epileptic seizures (ESs) diagnosed in our adult epilepsy monitoring unit. A multivariate analysis of covariance (MANCOVA) was used to determine whether groups with PNES and ES differed with respect to the percentage of complaints in the ROS questionnaire. Tenfold cross-validation was used to evaluate the predictive error of a logistic regression classifier for PNES status based on the percentage of positive complaints in the ROS questionnaire. A total of 44 patients were included for analysis. Patients with PNESs had a significantly higher number of complaints in the ROS questionnaire compared to patients with epilepsy. A threshold of 17% positive complaints achieved a 78% specificity and 85% sensitivity for discriminating between PNESs and ESs. We conclude that the routine ROS questionnaire may be a sensitive and specific predictive tool for discriminating between PNESs and ESs. Published by Elsevier Inc.

Maternal exposure to heatwave and preterm birth in Brisbane, Australia.

PubMed

Wang, J; Williams, G; Guo, Y; Pan, X; Tong, S

2013-12-01

To quantify the short-term effects of maternal exposure to heatwave on preterm birth. An ecological study. A population-based study in Brisbane, Australia. All pregnant women who had a spontaneous singleton live birth in Brisbane between November and March in 2000-2010 were studied. Daily data on pregnancy outcomes, meteorological factors, and ambient air pollutants were obtained. The Cox proportional hazards regression model with time-dependent variables was used to examine the short-term impact of heatwave on preterm birth. A series of cut-off temperatures and durations were used to define heatwave. Multivariable analyses were also performed to adjust for socio-economic factors, demographic factors, meteorological factors, and ambient air pollutants. Spontaneous preterm births. The adjusted hazard ratios (HRs) ranged from 1.13 (95% CI 1.03-1.24) to 2.00 (95% CI 1.37-2.91) by using different heatwave definitions, after controlling for demographic, socio-economic, and meteorological factors, and air pollutants. Heatwave was significantly associated with preterm birth: the associations were robust to the definitions of heatwave. The threshold temperatures, instead of duration, could be more likely to influence the evaluation of birth-related heatwaves. The findings of this study may have significant public health implications as climate change progresses. © 2013 RCOG.

Depression amongst carers of AIDS-orphaned and other-orphaned children in Umlazi Township, South Africa

PubMed Central

Kuo, Caroline; Operario, Don; Cluver, Lucie

2011-01-01

South Africa faces the challenge of supporting the well-being of adults caring for growing numbers of AIDS-orphaned children. These adults play a critical role in responses to the epidemic but little information exists in regards to their mental health needs. This paper reports on findings from n=1599 adults, recruited through representative household sampling, who serve as primary carers for children in Umlazi Township, a HIV endemic community. Overall, 22% of participants were carers of AIDS-orphaned children, 11% were carers of other-orphaned children, and 67% were carers of non-orphaned children. Prevalence of depression was 30.3%. Orphan carers, regardless of whether they cared for AIDS-orphaned and other-orphaned children, were significantly more likely than carers of non-orphaned children to meet the clinical threshold for depression (35.2% versus 27.9%, p<.01). In multivariate logistic regressions, food insecurity and being a female carer were identified as additional risk factors for greater depression. In contrast, households with access to running water and households dependent on salaries as the main source of income were identified as protective factors for disparities in depression. Mental health interventions are urgently needed to address an increased risk for depression amongst all orphan carers, not just those caring for AIDS-orphaned children. PMID:22081931

Evaluation of emphysema using three-dimensional computed tomography: association with postoperative complications in lung cancer patients.

PubMed

Kawakami, Kenichi; Iwano, Shingo; Hashimoto, Naozumi; Hasegawa, Yoshinori; Naganawa, Shinji

2015-02-01

Three-dimensional computed tomography (3D-CT) enables in vivo volumetry of total lung volume (TLV) and emphysematous low-attenuation volume (LAV) in patients with chronic obstructive pulmonary disease (COPD). We retrospectively investigated the correlation between preoperative 3D-CT volumetry and postoperative complications in lung cancer patients. We searched our institution's surgical records from December 2006 to December 2009 and selected patients who had undergone pulmonary lobectomy for primary lung cancer. From 3D-CT data, TLV and LAV <-950 HU of thresholds were retrospectively measured. The LAV% was calculated as follows: LAV% = LAV/TLV*100. The associations between the seven independent variables (LAV%, age, gender, body mass index, smoking history, forced expiratory volume in 1 second as percent forced vital capacity [FEV1%], and resected lobe) and the two outcomes (postoperative complications and prolonged postoperative stay [PPS]) were compared using logistic regression analysis. A total of 309 patients (222 males, 87 females; mean age, 67 years; range, 40-87 years) were evaluated. On multivariate analysis, age and LAV% were significantly correlated with postoperative complications (p = 0.006 and p = 0.006, respectively), and LAV% was significantly correlated with PPS (p = 0.031). LAV% measured using 3D-CT is more sensitive for predicting complications after lobectomy for lung cancer than FEV1%.

Optimizing Functional Network Representation of Multivariate Time Series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-09-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Optimizing Functional Network Representation of Multivariate Time Series

PubMed Central

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-01-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. PMID:22953051

Multivariate analysis of nystatin and metronidazole in a semi-solid matrix by means of diffuse reflectance NIR spectroscopy and PLS regression.

PubMed

Baratieri, Sabrina C; Barbosa, Juliana M; Freitas, Matheus P; Martins, José A

2006-01-23

A multivariate method of analysis of nystatin and metronidazole in a semi-solid matrix, based on diffuse reflectance NIR measurements and partial least squares regression, is reported. The product, a vaginal cream used in the antifungal and antibacterial treatment, is usually, quantitatively analyzed through microbiological tests (nystatin) and HPLC technique (metronidazole), according to pharmacopeial procedures. However, near infrared spectroscopy has demonstrated to be a valuable tool for content determination, given the rapidity and scope of the method. In the present study, it was successfully applied in the prediction of nystatin (even in low concentrations, ca. 0.3-0.4%, w/w, which is around 100,000 IU/5g) and metronidazole contents, as demonstrated by some figures of merit, namely linearity, precision (mean and repeatability) and accuracy.

Modelling lecturer performance index of private university in Tulungagung by using survival analysis with multivariate adaptive regression spline

NASA Astrophysics Data System (ADS)

Hasyim, M.; Prastyo, D. D.

2018-03-01

Survival analysis performs relationship between independent variables and survival time as dependent variable. In fact, not all survival data can be recorded completely by any reasons. In such situation, the data is called censored data. Moreover, several model for survival analysis requires assumptions. One of the approaches in survival analysis is nonparametric that gives more relax assumption. In this research, the nonparametric approach that is employed is Multivariate Regression Adaptive Spline (MARS). This study is aimed to measure the performance of private university’s lecturer. The survival time in this study is duration needed by lecturer to obtain their professional certificate. The results show that research activities is a significant factor along with developing courses material, good publication in international or national journal, and activities in research collaboration.

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

Influence of professional preparation and class structure on sexuality topics taught in middle and high schools.

PubMed

Rhodes, Darson L; Kirchofer, Gregg; Hammig, Bart J; Ogletree, Roberta J

2013-05-01

This study examined the impact of professional preparation and class structure on sexuality topics taught and use of practice-based instructional strategies in US middle and high school health classes. Data from the classroom-level file of the 2006 School Health Policies and Programs were used. A series of multivariable logistic regression models were employed to determine if sexuality content taught was dependent on professional preparation and /or class structure (HE only versus HE/another subject combined). Additional multivariable logistic regression models were employed to determine if use of practice-based instructional strategies was dependent upon professional preparation and/or class structure. Years of teaching health topics and size of the school district were included as covariates in the multivariable logistic regression models. Findings indicated professionally prepared health educators were significantly more likely to teach 7 of the 13 sexuality topics as compared to nonprofessionally prepared health educators. There was no statistically significant difference in the instructional strategies used by professionally prepared and nonprofessionally prepared health educators. Exclusively health education classes versus combined classes were significantly more likely to have included 6 of the 13 topics and to have incorporated practice-based instructional strategies in the curricula. This study indicated professional preparation and class structure impacted sexuality content taught. Class structure also impacted whether opportunities for students to practice skills were made available. Results support the need for continued advocacy for professionally prepared health educators and health only courses. © 2013, American School Health Association.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

A New Predictive Model of Centerline Segregation in Continuous Cast Steel Slabs by Using Multivariate Adaptive Regression Splines Approach

PubMed Central

García Nieto, Paulino José; González Suárez, Victor Manuel; Álvarez Antón, Juan Carlos; Mayo Bayón, Ricardo; Sirgo Blanco, José Ángel; Díaz Fernández, Ana María

2015-01-01

The aim of this study was to obtain a predictive model able to perform an early detection of central segregation severity in continuous cast steel slabs. Segregation in steel cast products is an internal defect that can be very harmful when slabs are rolled in heavy plate mills. In this research work, the central segregation was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. For this purpose, the most important physical-chemical parameters are considered. The results of the present study are two-fold. In the first place, the significance of each physical-chemical variable on the segregation is presented through the model. Second, a model for forecasting segregation is obtained. Regression with optimal hyperparameters was performed and coefficients of determination equal to 0.93 for continuity factor estimation and 0.95 for average width were obtained when the MARS technique was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter.

Association between cardiovascular risk factors and carotid intima-media thickness in prepubertal Brazilian children.

PubMed

Gazolla, Fernanda Mussi; Neves Bordallo, Maria Alice; Madeira, Isabel Rey; de Miranda Carvalho, Cecilia Noronha; Vieira Monteiro, Alexandra Maria; Pinheiro Rodrigues, Nádia Cristina; Borges, Marcos Antonio; Collett-Solberg, Paulo Ferrez; Muniz, Bruna Moreira; de Oliveira, Cecilia Lacroix; Pinheiro, Suellen Martins; de Queiroz Ribeiro, Rebeca Mathias

2015-05-01

Early exposure to cardiovascular risk factors creates a chronic inflammatory state that could damage the endothelium followed by thickening of the carotid intima-media. To investigate the association of cardiovascular risk factors and thickening of the carotid intima. Media in prepubertal children. In this cross-sectional study, carotid intima-media thickness (cIMT) and cardiovascular risk factors were assessed in 129 prepubertal children aged from 5 to 10 year. Association was assessed by simple and multivariate logistic regression analyses. In simple logistic regression analyses, body mass index (BMI) z-score, waist circumference, and systolic blood pressure (SBP) were positively associated with increased left, right, and average cIMT, whereas diastolic blood pressure was positively associated only with increased left and average cIMT (p<0.05). In multivariate logistic regression analyses increased left cIMT was positively associated to BMI z-score and SBP, and increased average cIMT was only positively associated to SBP (p<0.05). BMI z-score and SBP were the strongest risk factors for increased cIMT.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Characteristics and Outcomes of Pediatric Patients With Ebola Virus Disease Admitted to Treatment Units in Liberia and Sierra Leone: A Retrospective Cohort Study.

PubMed

Smit, Michael A; Michelow, Ian C; Glavis-Bloom, Justin; Wolfman, Vanessa; Levine, Adam C

2017-02-01

The clinical and virologic characteristics of Ebola virus disease (EVD) in children have not been thoroughly documented. Consecutive children aged <18 years with real-time polymerase chain reaction (RT-PCR)-confirmed EVD were enrolled retrospectively in 5 Ebola treatment units in Liberia and Sierra Leone in 2014/2015. Data collection and medical management were based on standardized International Medical Corps protocols. We performed descriptive statistics, multivariate logistic regression, and Kaplan-Meier survival analyses. Of 122 children enrolled, the median age was 7 years and one-third were aged <5 years. The female-to-male ratio was 1.3. The most common clinical features at triage and during hospitalization were fever, weakness, anorexia, and diarrhea, although 21% of patients were initially afebrile and 6 patients remained afebrile. Bleeding was rare at presentation (5%) and manifested subsequently in fewer than 50%. The overall case fatality rate was 57%. Factors associated with death in bivariate analyses were age <5 years, bleeding at any time during hospitalization, and high viral load. After adjustment with logistic regression modeling, the odds of death were 14.8-fold higher if patients were aged <5 years, 5-fold higher if the patient had any evidence of bleeding, and 5.2-fold higher if EVD RT-PCR cycle threshold value was ≤20. Plasmodium parasitemia had no impact on EVD outcomes. Age <5 years, bleeding, and high viral loads were poor prognostic indicators of children with EVD. Research to understand mechanisms of these risk factors and the impact of dehydration and electrolyte imbalance will improve health outcomes. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America.

Stress Hyperglycemia and Prognosis of Minor Ischemic Stroke and Transient Ischemic Attack: The CHANCE Study (Clopidogrel in High-Risk Patients With Acute Nondisabling Cerebrovascular Events).

PubMed

Pan, Yuesong; Cai, Xueli; Jing, Jing; Meng, Xia; Li, Hao; Wang, Yongjun; Zhao, Xingquan; Liu, Liping; Wang, David; Johnston, S Claiborne; Wei, Tiemin; Wang, Yilong

2017-11-01

We aimed to determine the association between stress hyperglycemia and risk of new stroke in patients with a minor ischemic stroke or transient ischemic attack. A subgroup of 3026 consecutive patients from 73 prespecified sites of the CHANCE trial (Clopidogrel in High-Risk Patients With Acute Nondisabling Cerebrovascular Events) were analyzed. Stress hyperglycemia was measured by glucose/glycated albumin (GA) ratio. Glucose/GA ratio was calculated by fasting plasma glucose divided by GA and categorized into 4 even groups according to the quartiles. The primary outcome was a new stroke (ischemic or hemorrhagic) at 90 days. We assessed the association between glucose/GA ratio and risk of stroke by multivariable Cox regression models adjusted for potential covariates. Among 3026 patients included, a total of 299 (9.9%) new stroke occurred at 3 months. Compared with patients with the lowest quartile, patients with the highest quartile of glucose/GA ratio was associated with an increased risk of stroke at 3 months after adjusted for potential covariates (12.0% versus 9.2%; adjusted hazard ratio, 1.46; 95% confidence interval, 1.06-2.01). Similar results were observed after further adjusted for fasting plasma glucose. We also observed that higher level of glucose/GA ratio was associated with an increased risk of stroke with a threshold of 0.29 using a Cox regression model with restricted cubic spline. Stress hyperglycemia, measured by glucose/GA ratio, was associated with an increased risk of stroke in patients with a minor ischemic stroke or transient ischemic attack. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00979589. © 2017 American Heart Association, Inc.

Long-Term Bone Marrow Suppression During Postoperative Chemotherapy in Rectal Cancer Patients After Preoperative Chemoradiation Therapy.

PubMed

Newman, Neil B; Sidhu, Manpreet K; Baby, Rekha; Moss, Rebecca A; Nissenblatt, Michael J; Chen, Ting; Lu, Shou-En; Jabbour, Salma K

2016-04-01

To quantify ensuing bone marrow (BM) suppression during postoperative chemotherapy resulting from preoperative chemoradiation (CRT) therapy for rectal cancer. We retrospectively evaluated 35 patients treated with preoperative CRT followed by postoperative 5-Fluorouracil and oxaliplatin (OxF) chemotherapy for locally advanced rectal cancer. The pelvic bone marrow (PBM) was divided into ilium (IBM), lower pelvis (LPBM), and lumbosacrum (LSBM). Dose volume histograms (DVH) measured the mean doses and percentage of BM volume receiving between 5-40 Gy (i.e.: PBM-V5, LPBM-V5). The Wilcoxon signed rank tests evaluated the differences in absolute hematologic nadirs during neoadjuvant vs. adjuvant treatment. Logistic regressions evaluated the association between dosimetric parameters and ≥ grade 3 hematologic toxicity (HT3) and hematologic event (HE) defined as ≥ grade 2 HT and a dose reduction in OxF. Receiver Operator Characteristic (ROC) curves were constructed to determine optimal threshold values leading to HT3. During OxF chemotherapy, 40.0% (n=14) and 48% (n=17) of rectal cancer patients experienced HT3 and HE, respectively. On multivariable logistic regression, increasing pelvic mean dose (PMD) and lower pelvis mean dose (LPMD) along with increasing PBM-V (25-40), LPBM-V25, and LPBM-V40 were significantly associated with HT3 and/or HE during postoperative chemotherapy. Exceeding ≥36.6 Gy to the PMD and ≥32.6 Gy to the LPMD strongly correlated with causing HT3 during postoperative chemotherapy. Neoadjuvant RT for rectal cancer has lasting effects on the pelvic BM, which are demonstrable during adjuvant OxF. Sparing of the BM during preoperative CRT can aid in reducing significant hematologic adverse events and aid in tolerance of postoperative chemotherapy. Copyright © 2016 Elsevier Inc. All rights reserved.

Phase Composition Maps integrate mineral compositions with rock textures from the micro-meter to the thin section scale

NASA Astrophysics Data System (ADS)

Willis, Kyle V.; Srogi, LeeAnn; Lutz, Tim; Monson, Frederick C.; Pollock, Meagen

2017-12-01

Textures and compositions are critical information for interpreting rock formation. Existing methods to integrate both types of information favor high-resolution images of mineral compositions over small areas or low-resolution images of larger areas for phase identification. The method in this paper produces images of individual phases in which textural and compositional details are resolved over three orders of magnitude, from tens of micrometers to tens of millimeters. To construct these images, called Phase Composition Maps (PCMs), we make use of the resolution in backscattered electron (BSE) images and calibrate the gray scale values with mineral analyses by energy-dispersive X-ray spectrometry (EDS). The resulting images show the area of a standard thin section (roughly 40 mm × 20 mm) with spatial resolution as good as 3.5 μm/pixel, or more than 81 000 pixels/mm2, comparable to the resolution of X-ray element maps produced by wavelength-dispersive spectrometry (WDS). Procedures to create PCMs for mafic igneous rocks with multivariate linear regression models for minerals with solid solution (olivine, plagioclase feldspar, and pyroxenes) are presented and are applicable to other rock types. PCMs are processed using threshold functions based on the regression models to image specific composition ranges of minerals. PCMs are constructed using widely-available instrumentation: a scanning-electron microscope (SEM) with BSE and EDS X-ray detectors and standard image processing software such as ImageJ and Adobe Photoshop. Three brief applications illustrate the use of PCMs as petrologic tools: to reveal mineral composition patterns at multiple scales; to generate crystal size distributions for intracrystalline compositional zones and compare growth over time; and to image spatial distributions of minerals at different stages of magma crystallization by integrating textures and compositions with thermodynamic modeling.

Frontal plane hip and ankle sensorimotor function, not age, predicts unipedal stance time

PubMed Central

Allet, Lara; Kim, Hogene; Ashton-Miller, James; De Mott, Trina; Richardson, James K.

2011-01-01

Introduction Changes occur in muscles and nerves with aging. This study aimed to explore the relationship between unipedal stance time (UST) and frontal plane hip and ankle sensorimotor function in subjects with diabetic neuropathy. Methods UST, quantitative measures of frontal plane ankle proprioceptive thresholds, and ankle and hip motor function were tested in forty-one persons with a spectrum of lower limb sensorimotor function, ranging from healthy to moderately severe diabetic neuropathy. Results Frontal plane hip and ankle sensorimotor function demonstrated significant relationships with UST. Multivariate analysis identified only composite hip strength, composite ankle proprioceptive threshold, and age to be significant predictors of UST (R2=0.73); they explained 46%, 24% and 3% of the variance, respectively. Discussion/Conclusions Frontal plane hip strength was the single best predictor of UST and appeared to compensate for less precise ankle proprioceptive thresholds. This finding is clinically relevant given the possibility of strengthening the hip, even in patients with significant PN. . PMID:22431092

Frontal plane hip and ankle sensorimotor function, not age, predicts unipedal stance time.

PubMed

Allet, Lara; Kim, Hogene; Ashton-Miller, James; De Mott, Trina; Richardson, James K

2012-04-01

Changes occur in muscles and nerves with aging. In this study we explore the relationship between unipedal stance time (UST) and frontal plane hip and ankle sensorimotor function in subjects with diabetic neuropathy. UST, quantitative measures of frontal plane ankle proprioceptive thresholds, and ankle and hip motor function were tested in 41 subjects with a spectrum of lower limb sensorimotor function ranging from healthy to moderately severe diabetic neuropathy. Frontal plane hip and ankle sensorimotor function demonstrated significant relationships with UST. Multivariate analysis identified only composite hip strength, ankle proprioceptive threshold, and age to be significant predictors of UST (R(2) = 0.73), explaining 46%, 24%, and 3% of the variance, respectively. Frontal plane hip strength was the single best predictor of UST and appeared to compensate for less precise ankle proprioceptive thresholds. This finding is clinically relevant given the possibility of strengthening the hip, even in patients with significant peripheral neuropathy. Copyright © 2011 Wiley Periodicals, Inc.

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik

2016-03-01

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.

Aging, not menopause, is associated with higher prevalence of hyperuricemia among older women.

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

This work aims to study the associations, if any, of hyperuricemia, gout, and menopause status in the US population. Using multiyear data from the National Health and Nutrition Examination Survey, we performed unmatched comparisons and one to three age-matched comparisons of women aged 20 to 70 years with and without hyperuricemia (serum urate ≥6 mg/dL). Analyses were performed using survey-weighted multiple logistic regression and conditional logistic regression, respectively. Overall, there were 1,477 women with hyperuricemia. Age and serum urate were significantly correlated. In unmatched analyses (n = 9,573 controls), postmenopausal women were older, were heavier, and had higher prevalence of renal impairment, hypertension, diabetes, and hyperlipidemia. In multivariable regression, after accounting for age, body mass index, glomerular filtration rate, and diuretic use, menopause was associated with hyperuricemia (odds ratio, 1.36; 95% CI, 1.05-1.76; P = 0.002). In corresponding multivariable regression using age-matched data (n = 4,431 controls), the odds ratio for menopause was 0.94 (95% CI, 0.83-1.06). Current use of hormone therapy was not associated with prevalent hyperuricemia in both unmatched and matched analyses. Age is a better statistical explanation for the higher prevalence of hyperuricemia among older women than menopause status.

Motor excitability measurements: the influence of gender, body mass index, age and temperature in healthy controls.

PubMed

Casanova, I; Diaz, A; Pinto, S; de Carvalho, M

2014-04-01

The technique of threshold tracking to test axonal excitability gives information about nodal and internodal ion channel function. We aimed to investigate variability of the motor excitability measurements in healthy controls, taking into account age, gender, body mass index (BMI) and small changes in skin temperature. We examined the left median nerve of 47 healthy controls using the automated threshold-tacking program, QTRAC. Statistical multiple regression analysis was applied to test relationship between nerve excitability measurements and subject variables. Comparisons between genders did not find any significant difference (P>0.2 for all comparisons). Multiple regression analysis showed that motor amplitude decreases with age and temperature, stimulus-response slope decreases with age and BMI, and that accommodation half-time decrease with age and temperature. The changes related to demographic features on TRONDE protocol parameters are small and less important than in conventional nerve conduction studies. Nonetheless, our results underscore the relevance of careful temperature control, and indicate that interpretation of stimulus-response slope and accommodation half-time should take into account age and BMI. In contrast, gender is not of major relevance to axonal threshold findings in motor nerves. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

[Using fractional polynomials to estimate the safety threshold of fluoride in drinking water].

PubMed

Pan, Shenling; An, Wei; Li, Hongyan; Yang, Min

2014-01-01

To study the dose-response relationship between fluoride content in drinking water and prevalence of dental fluorosis on the national scale, then to determine the safety threshold of fluoride in drinking water. Meta-regression analysis was applied to the 2001-2002 national endemic fluorosis survey data of key wards. First, fractional polynomial (FP) was adopted to establish fixed effect model, determining the best FP structure, after that restricted maximum likelihood (REML) was adopted to estimate between-study variance, then the best random effect model was established. The best FP structure was first-order logarithmic transformation. Based on the best random effect model, the benchmark dose (BMD) of fluoride in drinking water and its lower limit (BMDL) was calculated as 0.98 mg/L and 0.78 mg/L. Fluoride in drinking water can only explain 35.8% of the variability of the prevalence, among other influencing factors, ward type was a significant factor, while temperature condition and altitude were not. Fractional polynomial-based meta-regression method is simple, practical and can provide good fitting effect, based on it, the safety threshold of fluoride in drinking water of our country is determined as 0.8 mg/L.

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding.

PubMed

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-05-28

To distinguish upper from lower gastrointestinal (GI) bleeding. Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL.

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding

PubMed Central

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-01-01

AIM: To distinguish upper from lower gastrointestinal (GI) bleeding. METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. RESULTS: Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. CONCLUSION: The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL. PMID:26034359

Locally Weighted Score Estimation for Quantile Classification in Binary Regression Models

PubMed Central

Rice, John D.; Taylor, Jeremy M. G.

2016-01-01

One common use of binary response regression methods is classification based on an arbitrary probability threshold dictated by the particular application. Since this is given to us a priori, it is sensible to incorporate the threshold into our estimation procedure. Specifically, for the linear logistic model, we solve a set of locally weighted score equations, using a kernel-like weight function centered at the threshold. The bandwidth for the weight function is selected by cross validation of a novel hybrid loss function that combines classification error and a continuous measure of divergence between observed and fitted values; other possible cross-validation functions based on more common binary classification metrics are also examined. This work has much in common with robust estimation, but diers from previous approaches in this area in its focus on prediction, specifically classification into high- and low-risk groups. Simulation results are given showing the reduction in error rates that can be obtained with this method when compared with maximum likelihood estimation, especially under certain forms of model misspecification. Analysis of a melanoma data set is presented to illustrate the use of the method in practice. PMID:28018492

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Long-term ambient concentrations of total suspended particulates and oxidants as related to incidence of chronic disease in California Seventh-Day Adventists.

PubMed

Abbey, D E; Mills, P K; Petersen, F F; Beeson, W L

1991-08-01

Cancer incidence and mortality in a cohort of 6000 nonsmoking California Seventh-Day Adventists were monitored for a 6-year period, and relationships with long-term cumulative ambient air pollution were observed. Total suspended particulates (TSP) and ozone were measured in terms of numbers of hours in excess of several threshold levels corresponding to national standards as well as mean concentration. For all malignant neoplasms among females, risk increased with increasing exceedance frequencies of all thresholds of TSP except the lowest one, and those increased risks were highly statistically significant. For respiratory cancers, increased risk was associated with only one threshold of ozone, and this result was of borderline significance. Respiratory disease symptoms were assessed in 1977 and again in 1987 using the National Heart, Lung and Blood Institute respiratory symptoms questionnaire on a subcohort of 3914 individuals. Multivariate analyses which adjusted for past and passive smoking and occupational exposures indicated statistically significantly (p less than 0.05) elevated relative risks ranging up to 1.7 for incidence of asthma, definite symptoms of airway obstructive disease, and chronic bronchitis with TSP in excess of all thresholds except the lowest one but not for any thresholds of ozone. A trend association (p = 0.056) was noted between the threshold of 10 pphm ozone and incidence of asthma. These results are presented within the context of standards setting for these constituents of air pollution.

Long-term ambient concentrations of total suspended particulates and oxidants as related to incidence of chronic disease in California Seventh-Day Adventists.

PubMed Central

Abbey, D E; Mills, P K; Petersen, F F; Beeson, W L

1991-01-01

Cancer incidence and mortality in a cohort of 6000 nonsmoking California Seventh-Day Adventists were monitored for a 6-year period, and relationships with long-term cumulative ambient air pollution were observed. Total suspended particulates (TSP) and ozone were measured in terms of numbers of hours in excess of several threshold levels corresponding to national standards as well as mean concentration. For all malignant neoplasms among females, risk increased with increasing exceedance frequencies of all thresholds of TSP except the lowest one, and those increased risks were highly statistically significant. For respiratory cancers, increased risk was associated with only one threshold of ozone, and this result was of borderline significance. Respiratory disease symptoms were assessed in 1977 and again in 1987 using the National Heart, Lung and Blood Institute respiratory symptoms questionnaire on a subcohort of 3914 individuals. Multivariate analyses which adjusted for past and passive smoking and occupational exposures indicated statistically significantly (p less than 0.05) elevated relative risks ranging up to 1.7 for incidence of asthma, definite symptoms of airway obstructive disease, and chronic bronchitis with TSP in excess of all thresholds except the lowest one but not for any thresholds of ozone. A trend association (p = 0.056) was noted between the threshold of 10 pphm ozone and incidence of asthma. These results are presented within the context of standards setting for these constituents of air pollution. PMID:1954938

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Temperature dependence of needle and shoot elongation before bud break in Scots pine.

PubMed

Schiestl-Aalto, Pauliina; Mäkelä, Annikki

2017-03-01

Knowledge about the early part of needle growth is deficient compared with what is known about shoot growth. It is however important to understand growth of different organs to be able to estimate the changes in whole tree growth in a changing environment. The onset of growth in spring has been observed to occur over some certain threshold value of momentary temperature or temperature accumulation. We measured the length growth of Scots pine (Pinus sylvestris L.) needles and shoots from March until bud break over 3 years. We first compared needle growth with concurrent shoot growth. Then, we quantified threshold temperature of growth (i) with a logistic regression based on momentary temperatures and (ii) with the temperature sum accumulation method. Temperature sum was calculated with combinations of various time steps, starting dates and threshold temperature values. Needle elongation began almost concurrently with shoot elongation and proceeded linearly in relation to shoot growth until bud break. When studying the threshold temperature for growth, the method with momentary temperature effect on growth onset yielded ambiguous results in our conditions. The best fit of an exponential regression between needle growth or length and temperature sum was obtained with threshold temperatures -1 to +2 °C, with several combinations of starting date and time step. We conclude that although growth onset is a momentary event the process leading to it is a long-term continuum where past time temperatures have to be accounted for, rather than a sudden switch from quiescence to active growth. Further, our results indicate that lower temperatures than the commonly used +5 °C are sufficient for actuating the growth process. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Physiological and pathological characterization of capsaicin-induced reversible nerve degeneration and hyperalgesia.

PubMed

Chiang, H; Chang, K-C; Kan, H-W; Wu, S-W; Tseng, M-T; Hsueh, H-W; Lin, Y-H; Chao, C-C; Hsieh, S-T

2018-07-01

The study aimed to investigate the physiology, psychophysics, pathology and their relationship in reversible nociceptive nerve degeneration, and the physiology of acute hyperalgesia. We enrolled 15 normal subjects to investigate intraepidermal nerve fibre (IENF) density, contact heat-evoked potential (CHEP) and thermal thresholds during the capsaicin-induced skin nerve degeneration-regeneration; and CHEP and thermal thresholds at capsaicin-induced acute hyperalgesia. After 2-week capsaicin treatment, IENF density of skin was markedly reduced with reduced amplitude and prolonged latency of CHEP, and increased warm and heat pain thresholds. The time courses of skin nerve regeneration and reversal of physiology and psychophysics were different: IENF density was still lower at 10 weeks after capsaicin treatment than that at baseline, whereas CHEP amplitude and warm threshold became normalized within 3 weeks after capsaicin treatment. Although CHEP amplitude and IENF density were best correlated in a multiple linear regression model, a one-phase exponential association model showed better fit than a simple linear one, that is in the regeneration phase, the slope of the regression line between CHEP amplitude and IENF density was steeper in the subgroup with lower IENF densities than in the one with higher IENF densities. During capsaicin-induced hyperalgesia, recordable rate of CHEP to 43 °C heat stimulation was higher with enhanced CHEP amplitude and pain perception compared to baseline. There were differential restoration of IENF density, CHEP and thermal thresholds, and changed CHEP-IENF relationships during skin reinnervation. CHEP can be a physiological signature of acute hyperalgesia. These observations suggested the relationship between nociceptive nerve terminals and brain responses to thermal stimuli changed during different degree of skin denervation, and CHEP to low-intensity heat stimulus can reflect the physiology of hyperalgesia. © 2018 European Pain Federation - EFIC®.

Regression to the mean and alcohol consumption: a cohort study exploring implications for the interpretation of change in control groups in brief intervention trials.

PubMed

McCambridge, Jim; Kypri, Kypros; McElduff, Patrick

2014-02-01

Reductions in drinking among individuals randomised to control groups in brief alcohol intervention trials are common and suggest that asking study participants about their drinking may itself cause them to reduce their consumption. We sought to test the hypothesis that the statistical artefact regression to the mean (RTM) explains part of the reduction in such studies. 967 participants in a cohort study of alcohol consumption in New Zealand provided data at baseline and again six months later. We use graphical methods and apply thresholds of 8, 12, 16 and 20 in AUDIT scores to explore RTM. There was a negative association between baseline AUDIT scores and change in AUDIT scores from baseline to six months, which in the absence of bias and confounding, is RTM. Students with lower baseline scores tended to have higher follow-up scores and conversely, those with higher baseline scores tended to have lower follow-up scores. When a threshold score of 8 was used to select a subgroup, the observed mean change was approximately half of that observed without a threshold. The application of higher thresholds produced greater apparent reductions in alcohol consumption. Part of the reduction seen in the control groups of brief alcohol intervention trials is likely to be due to RTM and the amount of change is likely to be greater as the threshold for entry to the trial increases. Quantification of RTM warrants further study and should assist understanding assessment and other research participation effects. Copyright © 2013 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Thermoreceptive innervation of human glabrous and hairy skin: a contact heat evoked potential analysis.

PubMed

Granovsky, Yelena; Matre, Dagfinn; Sokolik, Alexander; Lorenz, Jürgen; Casey, Kenneth L

2005-06-01

The human palm has a lower heat detection threshold and a higher heat pain threshold than hairy skin. Neurophysiological studies of monkeys suggest that glabrous skin has fewer low threshold heat nociceptors (AMH type 2) than hairy skin. Accordingly, we used a temperature-controlled contact heat evoked potential (CHEP) stimulator to excite selectively heat receptors with C fibers or Adelta-innervated AMH type 2 receptors in humans. On the dorsal hand, 51 degrees C stimulation produced painful pinprick sensations and 41 degrees C stimuli evoked warmth. On the glabrous thenar, 41 degrees C stimulation produced mild warmth and 51 degrees C evoked strong but painless heat sensations. We used CHEP responses to estimate the conduction velocities (CV) of peripheral fibers mediating these sensations. On hairy skin, 41 degrees C stimuli evoked an ultra-late potential (mean, SD; N wave latency: 455 (118) ms) mediated by C fibers (CV by regression analysis: 1.28 m/s, N=15) whereas 51 degrees C stimuli evoked a late potential (N latency: 267 (33) ms) mediated by Adelta afferents (CV by within-subject analysis: 12.9 m/s, N=6). In contrast, thenar responses to 41 and 51 degrees C were mediated by C fibers (average N wave latencies 485 (100) and 433 (73) ms, respectively; CVs 0.95-1.35 m/s by regression analysis, N=15; average CV=1.7 (0.41) m/s calculated from distal glabrous and proximal hairy skin stimulation, N=6). The exploratory range of the human and monkey palm is enhanced by the abundance of low threshold, C-innervated heat receptors and the paucity of low threshold AMH type 2 heat nociceptors.

Co-occurrence of attention-deficit hyperactivity disorder symptoms with other psychopathology in young adults: parenting style as a moderator.

PubMed

Ni, Hsing-Chang; Gau, Susan Shur-Fen

2015-02-01

The extent to which parenting styles can influence secondary psychiatric symptoms among young adults with ADHD symptoms is unknown. This issue was investigated in a sample of 2284 incoming college students (male, 50.6%), who completed standardized questionnaires about adult ADHD symptoms, other DSM-IV symptoms, and their parents' parenting styles before their ages of 16. Among them, 2.8% and 22.8% were classified as having ADHD symptoms and sub-threshold ADHD symptoms, respectively. Logistic regression was used to compare the comorbid rates of psychiatric symptoms among the ADHD, sub-threshold ADHD and non-ADHD groups while multiple linear regressions were used to examine the moderating role of gender and parenting styles over the associations between ADHD and other psychiatric symptoms. Both ADHD groups were significantly more likely than other incoming students to have other DSM-IV symptoms. Parental care was negatively associated and parental overprotection/control positively associated with these psychiatric symptoms. Furthermore, significant interactions were found of parenting style with both threshold and sub-threshold ADHD in predicting wide-ranging comorbid symptoms. Specifically, the associations of ADHD with some externalizing symptoms were inversely related to level of paternal care, while associations of ADHD and sub-threshold ADHD with wide-ranging comorbid symptoms were positively related to level of maternal and paternal overprotection/control. These results suggest that parenting styles may modify the effects of ADHD on the risk of a wide range of temporally secondary DSM-IV symptoms among incoming college students, although other causal dynamics might be at work that need to be investigated in longitudinal studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Substituting values for censored data from Texas, USA, reservoirs inflated and obscured trends in analyses commonly used for water quality target development.

PubMed

Grantz, Erin; Haggard, Brian; Scott, J Thad

2018-06-12

We calculated four median datasets (chlorophyll a, Chl a; total phosphorus, TP; and transparency) using multiple approaches to handling censored observations, including substituting fractions of the quantification limit (QL; dataset 1 = 1QL, dataset 2 = 0.5QL) and statistical methods for censored datasets (datasets 3-4) for approximately 100 Texas, USA reservoirs. Trend analyses of differences between dataset 1 and 3 medians indicated percent difference increased linearly above thresholds in percent censored data (%Cen). This relationship was extrapolated to estimate medians for site-parameter combinations with %Cen > 80%, which were combined with dataset 3 as dataset 4. Changepoint analysis of Chl a- and transparency-TP relationships indicated threshold differences up to 50% between datasets. Recursive analysis identified secondary thresholds in dataset 4. Threshold differences show that information introduced via substitution or missing due to limitations of statistical methods biased values, underestimated error, and inflated the strength of TP thresholds identified in datasets 1-3. Analysis of covariance identified differences in linear regression models relating transparency-TP between datasets 1, 2, and the more statistically robust datasets 3-4. Study findings identify high-risk scenarios for biased analytical outcomes when using substitution. These include high probability of median overestimation when %Cen > 50-60% for a single QL, or when %Cen is as low 16% for multiple QL's. Changepoint analysis was uniquely vulnerable to substitution effects when using medians from sites with %Cen > 50%. Linear regression analysis was less sensitive to substitution and missing data effects, but differences in model parameters for transparency cannot be discounted and could be magnified by log-transformation of the variables.

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

The time frame of Epstein-Barr virus latent membrane protein-1 gene to disappear in nasopharyngeal swabs after initiation of primary radiotherapy is an independently significant prognostic factor predicting local control for patients with nasopharyngeal carcinoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, S.-Y.; Chang, K.-P.; Graduate Institute of Clinical Medical Sciences, Chang Gung University, Linkou, Taiwan

Purpose: The presence of Epstein-Barr virus latent membrane protein-1 (LMP-1) gene in nasopharyngeal swabs indicates the presence of nasopharyngeal carcinoma (NPC) mucosal tumor cells. This study was undertaken to investigate whether the time taken for LMP-1 to disappear after initiation of primary radiotherapy (RT) was inversely associated with NPC local control. Methods and Materials: During July 1999 and October 2002, there were 127 nondisseminated NPC patients receiving serial examinations of nasopharyngeal swabbing with detection of LMP-1 during the RT course. The time for LMP-1 regression was defined as the number of days after initiation of RT for LMP-1 results tomore » turn negative. The primary outcome was local control, which was represented by freedom from local recurrence. Results: The time for LMP-1 regression showed a statistically significant influence on NPC local control both univariately (p < 0.0001) and multivariately (p = 0.004). In multivariate analysis, the administration of chemotherapy conferred a significantly more favorable local control (p = 0.03). Advanced T status ({>=} T2b), overall treatment time of external photon radiotherapy longer than 55 days, and older age showed trends toward being poor prognosticators. The time for LMP-1 regression was very heterogeneous. According to the quartiles of the time for LMP-1 regression, we defined the pattern of LMP-1 regression as late regression if it required 40 days or more. Kaplan-Meier plots indicated that the patients with late regression had a significantly worse local control than those with intermediate or early regression (p 0.0129). Conclusion: Among the potential prognostic factors examined in this study, the time for LMP-1 regression was the most independently significant factor that was inversely associated with NPC local control.« less

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

NASA Astrophysics Data System (ADS)

Otero, Noelia Felipe; Sillmann, Jana; Schnell, Jordan L.; Rust, Henning W.; Butler, Tim

2016-04-01

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998-2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8-hour average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over Southern Europe. In general, the best model performance is found over Central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months.

Improving Image Quality of Coronary Computed Tomography Angiography Using Patient Weight and Height-Dependent Scan Trigger Threshold.

PubMed

Kang, Deqiang; Hua, Haiqin; Peng, Nan; Zhao, Jing; Wang, Zhiqun

2017-04-01

We aim to improve the image quality of coronary computed tomography angiography (CCTA) by using personalized weight and height-dependent scan trigger threshold. This study was divided into two parts. First, we performed and analyzed the 100 scheduled CCTA data, which were acquired by using body mass index-dependent Smart Prep sequence (trigger threshold ranged from 80 Hu to 250 Hu based on body mass index). By identifying the cases of high quality image, a linear regression equation was established to determine the correlation among the Smart Prep threshold, height, and body weight. Furthermore, a quick search table was generated for weight and height-dependent Smart Prep threshold in CCTA scan. Second, to evaluate the effectiveness of the new individual threshold method, an additional 100 consecutive patients were divided into two groups: individualized group (n = 50) with weight and height-dependent threshold and control group (n = 50) with the conventional constant threshold of 150 HU. Image quality was compared between the two groups by measuring the enhancement in coronary artery, aorta, left and right ventricle, and inferior vena cava. By visual inspection, image quality scores were performed to compare between the two groups. Regression equation between Smart Prep threshold (K, Hu), height (H, cm), and body weight (BW, kg) was K = 0.811 × H + 1.917 × BW - 99.341. When compared to the control group, the individualized group presented an average overall increase of 12.30% in enhancement in left main coronary artery, 12.94% in proximal right coronary artery, and 10.6% in aorta. Correspondingly, the contrast-to-noise ratios increased by 26.03%, 27.08%, and 23.17%, respectively, and by 633.1% in contrast between aorta and left ventricle. Meanwhile, the individualized group showed an average overall decrease of 22.7% in enhancement of right ventricle and 32.7% in inferior vena cava. There was no significant difference of the image noise between the two groups (P > .05). By visual inspection, the image quality score of the individualized group was higher than that of the control group. Using personalized weight and height-dependent Smart Prep threshold to adjust scan trigger time can significantly improve the image quality of CCTA. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Determining the Threshold for HbA1c as a Predictor for Adverse Outcomes After Total Joint Arthroplasty: A Multicenter, Retrospective Study.

PubMed

Tarabichi, Majd; Shohat, Noam; Kheir, Michael M; Adelani, Muyibat; Brigati, David; Kearns, Sean M; Patel, Pankajkumar; Clohisy, John C; Higuera, Carlos A; Levine, Brett R; Schwarzkopf, Ran; Parvizi, Javad; Jiranek, William A

2017-09-01

Although HbA1c is commonly used for assessing glycemic control before surgery, there is no consensus regarding its role and the appropriate threshold in predicting adverse outcomes. This study was designed to evaluate the potential link between HbA1c and subsequent periprosthetic joint infection (PJI), with the intention of determining the optimal threshold for HbA1c. This is a multicenter retrospective study, which identified 1645 diabetic patients who underwent primary total joint arthroplasty (1004 knees and 641 hips) between 2001 and 2015. All patients had an HbA1c measured within 3 months of surgery. The primary outcome of interest was a PJI at 1 year based on the Musculoskeletal Infection Society criteria. Secondary outcomes included orthopedic (wound and mechanical complications) and nonorthopedic complications (sepsis, thromboembolism, genitourinary, and cardiovascular complications). A regression analysis was performed to determine the independent influence of HbA1c for predicting PJI. Overall 22 cases of PJI occurred at 1 year (1.3%). HbA1c at a threshold of 7.7 was distinct for predicting PJI (area under the curve, 0.65; 95% confidence interval, 0.51-0.78). Using this threshold, PJI rates increased from 0.8% (11 of 1441) to 5.4% (11 of 204). In the stepwise logistic regression analysis, PJI remained the only variable associated with higher HbA1c (odds ratio, 1.5; confidence interval, 1.2-2.0; P = .0001). There was no association between high HbA1c levels and other complications assessed. High HbA1c levels are associated with an increased risk for PJI. A threshold of 7.7% seems to be more indicative of infection than the commonly used 7% and should perhaps be the goal in preoperative patient optimization. Copyright © 2017 Elsevier Inc. All rights reserved.

Geodesic least squares regression for scaling studies in magnetic confinement fusion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verdoolaege, Geert

In regression analyses for deriving scaling laws that occur in various scientific disciplines, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to scaling laws. We here discuss a new regression method that is robust in the presence of significant uncertainty on both the data and the regression model. The method, which we call geodesic least squares regression (GLS), is based on minimization of the Rao geodesic distance on a probabilistic manifold. We demonstrate the superiority ofmore » the method using synthetic data and we present an application to the scaling law for the power threshold for the transition to the high confinement regime in magnetic confinement fusion devices.« less

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Empirical Bayes approach to the estimation of "unsafety": the multivariate regression method.

PubMed

Hauer, E

1992-10-01

There are two kinds of clues to the unsafety of an entity: its traits (such as traffic, geometry, age, or gender) and its historical accident record. The Empirical Bayes approach to unsafety estimation makes use of both kinds of clues. It requires information about the mean and the variance of the unsafety in a "reference population" of similar entities. The method now in use for this purpose suffers from several shortcomings. First, a very large reference population is required. Second, the choice of reference population is to some extent arbitrary. Third, entities in the reference population usually cannot match the traits of the entity the unsafety of which is estimated. To alleviate these shortcomings the multivariate regression method for estimating the mean and variance of unsafety in reference populations is offered. Its logical foundations are described and its soundness is demonstrated. The use of the multivariate method makes the Empirical Bayes approach to unsafety estimation applicable to a wider range of circumstances and yields better estimates of unsafety. The application of the method to the tasks of identifying deviant entities and of estimating the effect of interventions on unsafety are discussed and illustrated by numerical examples.

A multivariate analysis of clinical and morphological prognostic factors in squamous cell carcinoma of the vulva.

PubMed

Smyczek-Gargya, B; Volz, B; Geppert, M; Dietl, J

1997-01-01

Clinical and histological data of 168 patients with squamous cell carcinoma of the vulva were analyzed with respect to survival. 151 patients underwent surgery, 12 patients were treated with primary radiation and in 5 patients no treatment was performed. Follow-up lasted from at least 2 up to 22 years' posttreatment. In univariate analysis, the following factors were highly significant: presurgery lymph node status, tumor infiltration beyond the vulva, tumor grading, histological inguinal lymph node status, pre- and postsurgery tumor stage, depth of invasion and tumor diameter. In the multivariate analysis (Cox regression), the most powerful factors were shown to be histological inguinal lymph node status, tumor diameter and tumor grading. The multivariate logistic regression analysis worked out as main prognostic factors for metastases of inguinal lymph nodes: presurgery inguinal lymph node status, tumor size, depth of invasion and tumor grading. Based on these results, tumor biology seems to be the decisive factor concerning recurrence and survival. Therefore, we suggest a more conservative treatment of vulvar carcinoma. Patients with confined carcinoma to the vulva, with a tumor diameter up to 3 cm and without clinical suspected lymph nodes, should be treated by wide excision/partial vulvectomy with ipsilateral lymphadenectomy.

Low-Dose N,N-Dimethylformamide Exposure and Liver Injuries in a Cohort of Chinese Leather Industry Workers.

PubMed

Qi, Cong; Gu, Yiyang; Sun, Qing; Gu, Hongliang; Xu, Bo; Gu, Qing; Xiao, Jing; Lian, Yulong

2017-05-01

We assessed the risk of liver injuries following low doses of N,N-dimethylformamide (DMF) below threshold limit values (20 mg/m) among leather industry workers and comparison groups. A cohort of 429 workers from a leather factory and 466 non-exposed subjects in China were followed for 4 years. Poisson regression and piece-wise linear regression were used to examine the relationship between DMF and liver injury. Workers exposed to a cumulative dose of DMF were significantly more likely than non-exposed workers to develop liver injury. A nonlinear relationship between DMF and liver injury was observed, and a threshold of the cumulative DMF dose for liver injury was 7.30 (mg/m) year. The findings indicate the importance of taking action to reduce DMF occupational exposure limits for promoting worker health.

Patient cost-sharing, socioeconomic status, and children's health care utilization.

PubMed

Nilsson, Anton; Paul, Alexander

2018-05-01

This paper estimates the effect of cost-sharing on the demand for children's and adolescents' use of medical care. We use a large population-wide registry dataset including detailed information on contacts with the health care system as well as family income. Two different estimation strategies are used: regression discontinuity design exploiting age thresholds above which fees are charged, and difference-in-differences models exploiting policy changes. We also estimate combined regression discontinuity difference-in-differences models that take into account discontinuities around age thresholds caused by factors other than cost-sharing. We find that when care is free of charge, individuals increase their number of doctor visits by 5-10%. Effects are similar in middle childhood and adolescence, and are driven by those from low-income families. The differences across income groups cannot be explained by other factors that correlate with income, such as maternal education. Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of fatigue on motor unit firing rate versus recruitment threshold relationships.

PubMed

Stock, Matt S; Beck, Travis W; Defreitas, Jason M

2012-01-01

The purpose of this study was to examine the influence of fatigue on the average firing rate versus recruitment threshold relationships for the vastus lateralis (VL) and vastus medialis. Nineteen subjects performed ten maximum voluntary contractions of the dominant leg extensors. Before and after this fatiguing protocol, the subjects performed a trapezoid isometric muscle action of the leg extensors, and bipolar surface electromyographic signals were detected from both muscles. These signals were then decomposed into individual motor unit action potential trains. For each subject and muscle, the relationship between average firing rate and recruitment threshold was examined using linear regression analyses. For the VL, the linear slope coefficients and y-intercepts for these relationships increased and decreased, respectively, after fatigue. For both muscles, many of the motor units decreased their firing rates. With fatigue, recruitment of higher threshold motor units resulted in an increase in slope for the VL. Copyright © 2011 Wiley Periodicals, Inc.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception.

PubMed

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness.

Neural Activity Patterns in the Human Brain Reflect Tactile Stickiness Perception

PubMed Central

Kim, Junsuk; Yeon, Jiwon; Ryu, Jaekyun; Park, Jang-Yeon; Chung, Soon-Cheol; Kim, Sung-Phil

2017-01-01

Our previous human fMRI study found brain activations correlated with tactile stickiness perception using the uni-variate general linear model (GLM) (Yeon et al., 2017). Here, we conducted an in-depth investigation on neural correlates of sticky sensations by employing a multivoxel pattern analysis (MVPA) on the same dataset. In particular, we statistically compared multi-variate neural activities in response to the three groups of sticky stimuli: A supra-threshold group including a set of sticky stimuli that evoked vivid sticky perception; an infra-threshold group including another set of sticky stimuli that barely evoked sticky perception; and a sham group including acrylic stimuli with no physically sticky property. Searchlight MVPAs were performed to search for local activity patterns carrying neural information of stickiness perception. Similar to the uni-variate GLM results, significant multi-variate neural activity patterns were identified in postcentral gyrus, subcortical (basal ganglia and thalamus), and insula areas (insula and adjacent areas). Moreover, MVPAs revealed that activity patterns in posterior parietal cortex discriminated the perceptual intensities of stickiness, which was not present in the uni-variate analysis. Next, we applied a principal component analysis (PCA) to the voxel response patterns within identified clusters so as to find low-dimensional neural representations of stickiness intensities. Follow-up clustering analyses clearly showed separate neural grouping configurations between the Supra- and Infra-threshold groups. Interestingly, this neural categorization was in line with the perceptual grouping pattern obtained from the psychophysical data. Our findings thus suggest that different stickiness intensities would elicit distinct neural activity patterns in the human brain and may provide a neural basis for the perception and categorization of tactile stickiness. PMID:28936171

Step-rate cut-points for physical activity intensity in patients with multiple sclerosis: The effect of disability status.

PubMed

Agiovlasitis, Stamatis; Sandroff, Brian M; Motl, Robert W

2016-02-15

Evaluating the relationship between step-rate and rate of oxygen uptake (VO2) may allow for practical physical activity assessment in patients with multiple sclerosis (MS) of differing disability levels. To examine whether the VO2 to step-rate relationship during over-ground walking differs across varying disability levels among patients with MS and to develop step-rate thresholds for moderate- and vigorous-intensity physical activity. Adults with MS (N=58; age: 51 ± 9 years; 48 women) completed one over-ground walking trial at comfortable speed, one at 0.22 m · s(-1) slower, and one at 0.22 m · s(-1) faster. Each trial lasted 6 min. VO2 was measured with portable spirometry and steps with hand-tally. Disability status was classified as mild, moderate, or severe based on Expanded Disability Status Scale scores. Multi-level regression indicated that step-rate, disability status, and height significantly predicted VO2 (p<0.05). Based on this model, we developed step-rate thresholds for activity intensity that vary by disability status and height. A separate regression without height allowed for development of step-rate thresholds that vary only by disability status. The VO2 during over-ground walking differs among ambulatory patients with MS based on disability level and height, yielding different step-rate thresholds for physical activity intensity. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of Frequency Drift on the Quantification of Gamma-Aminobutyric Acid Using MEGA-PRESS

NASA Astrophysics Data System (ADS)

Tsai, Shang-Yueh; Fang, Chun-Hao; Wu, Thai-Yu; Lin, Yi-Ru

2016-04-01

The MEGA-PRESS method is the most common method used to measure γ-aminobutyric acid (GABA) in the brain at 3T. It has been shown that the underestimation of the GABA signal due to B0 drift up to 1.22 Hz/min can be reduced by post-frequency alignment. In this study, we show that the underestimation of GABA can still occur even with post frequency alignment when the B0 drift is up to 3.93 Hz/min. The underestimation can be reduced by applying a frequency shift threshold. A total of 23 subjects were scanned twice to assess the short-term reproducibility, and 14 of them were scanned again after 2-8 weeks to evaluate the long-term reproducibility. A linear regression analysis of the quantified GABA versus the frequency shift showed a negative correlation (P < 0.01). Underestimation of the GABA signal was found. When a frequency shift threshold of 0.125 ppm (15.5 Hz or 1.79 Hz/min) was applied, the linear regression showed no statistically significant difference (P > 0.05). Therefore, a frequency shift threshold at 0.125 ppm (15.5 Hz) can be used to reduce underestimation during GABA quantification. For data with a B0 drift up to 3.93 Hz/min, the coefficients of variance of short-term and long-term reproducibility for the GABA quantification were less than 10% when the frequency threshold was applied.

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study

PubMed Central

Jackson, Rod

2017-01-01

Background Many national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients’ multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country’s total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population. Methods and results A synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30–84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each ‘synthetic’ person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources. Conclusions We were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere. PMID:28384217

Trace elements of concern affecting urban agriculture in industrialized areas: A multivariate approach.

PubMed

Boente, C; Matanzas, N; García-González, N; Rodríguez-Valdés, E; Gallego, J R

2017-09-01

The urban and peri-urban soils used for agriculture could be contaminated by atmospheric deposition or industrial releases, thus raising concerns about the potential risk to public health. Here we propose a method to evaluate potential soil pollution based on multivariate statistics, geostatistics (kriging), a novel soil pollution index, and bioavailability assessments. This approach was tested in two districts of a highly populated and industrialized city (Gijón, Spain). The soils showed anomalous content of several trace elements, such as As and Pb (up to 80 and 585 mg kg -1 respectively). In addition, factor analyses associated these elements with anthropogenic activity, whereas other elements were attributed to natural sources. Subsequent clustering also facilitated the differentiation between the northern area studied (only limited Pb pollution found) and the southern area (pattern of coal combustion, including simultaneous anomalies of trace elements and benzo(a)pyrene). A normalized soil pollution index (SPI) was calculated by kriging, using only the elements falling above threshold levels; therefore point-source polluted zones in the northern area and diffuse contamination in the south were identified. In addition, in the six mapping units with the highest SPIs of the fifty studied, we observed low bioavailability for most of the elements that surpassed the threshold levels. However, some anomalies of Pb contents and the pollution fingerprint in the central area of the southern grid call for further site-specific studies. On the whole, the combination of a multivariate (geo) statistic approach and a bioavailability assessment allowed us to efficiently identify sources of contamination and potential risks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developing a synthetic national population to investigate the impact of different cardiovascular disease risk management strategies: A derivation and validation study.

PubMed

Knight, Josh; Wells, Susan; Marshall, Roger; Exeter, Daniel; Jackson, Rod

2017-01-01

Many national cardiovascular disease (CVD) risk factor management guidelines now recommend that drug treatment decisions should be informed primarily by patients' multi-variable predicted risk of CVD, rather than on the basis of single risk factor thresholds. To investigate the potential impact of treatment guidelines based on CVD risk thresholds at a national level requires individual level data representing the multi-variable CVD risk factor profiles for a country's total adult population. As these data are seldom, if ever, available, we aimed to create a synthetic population, representing the joint CVD risk factor distributions of the adult New Zealand population. A synthetic population of 2,451,278 individuals, representing the actual age, gender, ethnicity and social deprivation composition of people aged 30-84 years who completed the 2013 New Zealand census was generated using Monte Carlo sampling. Each 'synthetic' person was then probabilistically assigned values of the remaining cardiovascular disease (CVD) risk factors required for predicting their CVD risk, based on data from the national census national hospitalisation and drug dispensing databases and a large regional cohort study, using Monte Carlo sampling and multiple imputation. Where possible, the synthetic population CVD risk distributions for each non-demographic risk factor were validated against independent New Zealand data sources. We were able to develop a synthetic national population with realistic multi-variable CVD risk characteristics. The construction of this population is the first step in the development of a micro-simulation model intended to investigate the likely impact of a range of national CVD risk management strategies that will inform CVD risk management guideline updates in New Zealand and elsewhere.

Using computer assisted image analysis to determine the optimal Ki67 threshold for predicting outcome of invasive breast cancer

PubMed Central

Tay, Timothy Kwang Yong; Thike, Aye Aye; Pathmanathan, Nirmala; Jara-Lazaro, Ana Richelia; Iqbal, Jabed; Sng, Adeline Shi Hui; Ye, Heng Seow; Lim, Jeffrey Chun Tatt; Koh, Valerie Cui Yun; Tan, Jane Sie Yong; Yeong, Joe Poh Sheng; Chow, Zi Long; Li, Hui Hua; Cheng, Chee Leong; Tan, Puay Hoon

2018-01-01

Background Ki67 positivity in invasive breast cancers has an inverse correlation with survival outcomes and serves as an immunohistochemical surrogate for molecular subtyping of breast cancer, particularly ER positive breast cancer. The optimal threshold of Ki67 in both settings, however, remains elusive. We use computer assisted image analysis (CAIA) to determine the optimal threshold for Ki67 in predicting survival outcomes and differentiating luminal B from luminal A breast cancers. Methods Quantitative scoring of Ki67 on tissue microarray (TMA) sections of 440 invasive breast cancers was performed using Aperio ePathology ImmunoHistochemistry Nuclear Image Analysis algorithm, with TMA slides digitally scanned via Aperio ScanScope XT System. Results On multivariate analysis, tumours with Ki67 ≥14% had an increased likelihood of recurrence (HR 1.941, p=0.021) and shorter overall survival (HR 2.201, p=0.016). Similar findings were observed in the subset of 343 ER positive breast cancers (HR 2.409, p=0.012 and HR 2.787, p=0.012 respectively). The value of Ki67 associated with ER+HER2-PR<20% tumours (Luminal B subtype) was found to be <17%. Conclusion Using CAIA, we found optimal thresholds for Ki67 that predict a poorer prognosis and an association with the Luminal B subtype of breast cancer. Further investigation and validation of these thresholds are recommended. PMID:29545924

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

Gallinat, Anja; Leerhoff, Sabine; Paul, Andreas; Molmenti, Ernesto P; Schulze, Maren; Witzke, Oliver; Sotiropoulos, Georgios C

2016-12-01

Elevated donor serum creatinine has been associated with inferior graft survival in kidney transplantation (KT). The aim of this study was to evaluate the impact of elevated donor serum creatinine on short and long-term outcomes and to determine possible ways to optimize the use of these organs. All kidney transplants from 01-2000 to 12-2012 with donor creatinine ≥ 2 mg/dl were considered. Risk factors for delayed graft function (DGF) were explored with uni- and multivariate regression analyses. Donor and recipient data were analyzed with uni- and multivariate cox proportional hazard analyses. Graft and patient survival were calculated using the Kaplan-Meier method. Seventy-eight patients were considered. Median recipient age and waiting time on dialysis were 53 years and 5.1 years, respectively. After a median follow-up of 6.2 years, 63 patients are alive. 1, 3, and 5-year graft and patient survival rates were 92, 89, and 89 % and 96, 93, and 89 %, respectively. Serum creatinine level at procurement and recipient's dialysis time prior to KT were predictors of DGF in multivariate analysis (p = 0.0164 and p = 0.0101, respectively). Charlson comorbidity score retained statistical significance by multivariate regression analysis for graft survival (p = 0.0321). Recipient age (p = 0.0035) was predictive of patient survival by multivariate analysis. Satisfactory long-term kidney transplant outcomes in the setting of elevated donor serum creatinine ≥2 mg/dl can be achieved when donor creatinine is <3.5 mg/dl, and the recipient has low comorbidities, is under 56 years of age, and remains in dialysis prior to KT for <6.8 years.

Red blood cell transfusion and increased length of storage are not associated with deep vein thrombosis in medical and surgical critically ill patients: a prospective observational cohort study

PubMed Central

2011-01-01

Introduction With prolonged storage times, cell membranes of red blood cells (RBCs) undergo morphologic and biochemical changes, termed 'RBC storage lesions'. Storage lesions may promote inflammation and thrombophilia when transfused. In trauma patients, RBC transfusion was an independent risk factor for deep vein thrombosis (DVT), specifically when RBC units were stored > 21 days or when 5 or more units were transfused. The objective of this study was to determine if RBC transfusions or RBC storage age predicts incident DVT in medical or surgical intensive care unit (ICU) patients. Methods Using a database which prospectively enrolled 261 patients over the course of 1 year with an ICU stay of at least 3 days, we analyzed DVT and RBC transfusions using Cox proportional hazards regression. Transfusions were analyzed with 4 thresholds, and storage age using 3 thresholds. DVTs were identified by twice-weekly proximal leg ultrasounds. Multivariable analyses were adjusted for 4 significant DVT predictors in this population (venous thrombosis history, chronic dialysis, platelet transfusion and inotropes). Results Of 261 patients, 126 (48.3%) had at least 1 RBC transfusion; 46.8% of those transfused had ≥ 5 units in ICU. Patients receiving RBCs were older (68.8 vs 64.1 years), more likely to be female (47.0 vs 30.7), sicker (APACHEII 26.8 vs 24.4), and more likely to be surgical (21.4 vs 8.9) (P < 0.05). The total number of RBCs per patient was 1-64, mean was 6.3 (SD 7.5), median was 4 (IQR 2,8). In univariate analyses, there was no association between DVT and RBC exposure (1 day earlier, 3 days earlier, 7 days earlier, or ever) or RBC storage (≤ 7 or > 7 days, ≤ 14 or > 14 days, ≤ 21 or > 21 days). Among patients transfused, no multivariable analyses showed that RBC transfusion or storage age predicted DVT. Trends were counter to the hypothesis (e.g., RBC storage for ≤ 7 days suggested a higher DVT risk compared to > 7 days (HR 5.3; 95% CI 1.3-22.1). Conclusions We were unable to detect any association between RBC transfusions or prolonged red cell storage and increased risk of DVT in medical or surgical ICU patients. Alternate explanations include a lack of sufficient events or patients' interaction, between patient groups, a mixing of red cell storage times creating differential effects on DVT risk, and unmeasured confounders. PMID:22044745

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

NASA Astrophysics Data System (ADS)

Drzewiecki, Wojciech

2017-12-01

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.

PubMed Central

Derouin, F.; Garin, Y. J.; Buffard, C.; Berthelot, F.; Petithory, J. C.

1994-01-01

A collaborative study conducted by the French National Agency for Quality Control in Parasitology (CNQP) and various manufacturers of ELISA kits, represented by the Association of Laboratory Reagent Manufacturers (SFRL) compared the toxoplasmosis IgG antibody titres obtained with different ELISA-IgG kits and determined the relationships between the titres obtained by these techniques and the titre defined in international units (IU). Fifty-one serum samples with toxoplasmosis antibody titres ranging from 0 to 900 IU were tested in two successive studies with 16 ELISA-IgG kits. For the negative sera, false-positive reactions were observed with one kit. For the positive sera, the titres observed in ELISA were generally higher than those expressed in IU. Above 250 IU, the very wide variability of the titres found with the different ELISA kits renders any comparative analysis impossible. For titres below 250 IU, the results are sufficiently homogeneous to permit the use of regression analysis to study how the results for each ELISA kit compare with the mean results for the other kits. The slope of the line of regression shows a tendency to over-titration or under-titration compared with the results of the other manufacturers; the ordinate at the origin reflects the positivity threshold of the reaction and can be used to assess the risk of a lack of sensitivity (high threshold) or of specificity (threshold too low). On the whole, the trends revealed for a given manufacturer are constant from one study to the other. Within this range of titres, regression analysis also reveals the general tendency of ELISA kits to overestimate the titres by comparison with immunofluorescence.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:8205645

Estimation of railroad capacity using parametric methods.

DOT National Transportation Integrated Search

2013-12-01

This paper reviews different methodologies used for railroad capacity estimation and presents a user-friendly method to measure capacity. The objective of this paper is to use multivariate regression analysis to develop a continuous relation of the d...

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

Computational Visual Stress Level Analysis of Calcareous Algae Exposed to Sedimentation

PubMed Central

Nilssen, Ingunn; Eide, Ingvar; de Oliveira Figueiredo, Marcia Abreu; de Souza Tâmega, Frederico Tapajós; Nattkemper, Tim W.

2016-01-01

This paper presents a machine learning based approach for analyses of photos collected from laboratory experiments conducted to assess the potential impact of water-based drill cuttings on deep-water rhodolith-forming calcareous algae. This pilot study uses imaging technology to quantify and monitor the stress levels of the calcareous algae Mesophyllum engelhartii (Foslie) Adey caused by various degrees of light exposure, flow intensity and amount of sediment. A machine learning based algorithm was applied to assess the temporal variation of the calcareous algae size (∼ mass) and color automatically. Measured size and color were correlated to the photosynthetic efficiency (maximum quantum yield of charge separation in photosystem II, ΦPSIImax) and degree of sediment coverage using multivariate regression. The multivariate regression showed correlations between time and calcareous algae sizes, as well as correlations between fluorescence and calcareous algae colors. PMID:27285611

Factors affecting the outcome of excimer laser photorefractive keratectomy: a preliminary multivariable regression analysis

NASA Astrophysics Data System (ADS)

Maguen, Ezra I.; Papaioannou, Thanassis; Nesburn, Anthony B.; Salz, James J.; Warren, Cathy; Grundfest, Warren S.

1996-05-01

Multivariable regression analysis was used to evaluate the combined effects of some preoperative and operative variables on the change of refraction following excimer laser photorefractive keratectomy for myopia (PRK). This analysis was performed on 152 eyes (at 6 months postoperatively) and 156 eyes (at 12 months postoperatively). The following variables were considered: intended refractive correction, patient age, treatment zone, central corneal thickness, average corneal curvature, and intraocular pressure. At 6 months after surgery, the cumulative R2 was 0.43 with 0.38 attributed to the intended correction and 0.06 attributed to the preoperative corneal curvature. At 12 months, the cumulative R2 was 0.37 where 0.33 was attributed to the intended correction, 0.02 to the preoperative corneal curvature, and 0.01 to both preoperative corneal thickness and to the patient age. Further model augmentation is necessary to account for the remaining variability and the behavior of the residuals.

A multivariate regression model for detection of fumonisins content in maize from near infrared spectra.

PubMed

Giacomo, Della Riccia; Stefania, Del Zotto

2013-12-15

Fumonisins are mycotoxins produced by Fusarium species that commonly live in maize. Whereas fungi damage plants, fumonisins cause disease both to cattle breedings and human beings. Law limits set fumonisins tolerable daily intake with respect to several maize based feed and food. Chemical techniques assure the most reliable and accurate measurements, but they are expensive and time consuming. A method based on Near Infrared spectroscopy and multivariate statistical regression is described as a simpler, cheaper and faster alternative. We apply Partial Least Squares with full cross validation. Two models are described, having high correlation of calibration (0.995, 0.998) and of validation (0.908, 0.909), respectively. Description of observed phenomenon is accurate and overfitting is avoided. Screening of contaminated maize with respect to European legal limit of 4 mg kg(-1) should be assured. Copyright © 2013 Elsevier Ltd. All rights reserved.

Specific prognostic factors for secondary pancreatic infection in severe acute pancreatitis.

PubMed

Armengol-Carrasco, M; Oller, B; Escudero, L E; Roca, J; Gener, J; Rodríguez, N; del Moral, P; Moreno, P

1999-01-01

The aim of the present study was to investigate whether there are specific prognostic factors to predict the development of secondary pancreatic infection (SPI) in severe acute pancreatitis in order to perform a computed tomography-fine needle aspiration with bacteriological sampling at the right moment and confirm the diagnosis. Twenty-five clinical and laboratory parameters were determined sequentially in 150 patients with severe acute pancreatitis (SAP) and univariate, and multivariate regression analyses were done looking for correlation with the development of SPI. Only APACHE II score and C-reactive protein levels were related to the development of SPI in the multivariate analysis. A regression equation was designed using these two parameters, and empiric cut-off points defined the subgroup of patients at high risk of developing secondary pancreatic infection. The results showed that it is possible to predict SPI during SAP allowing bacteriological confirmation and early treatment of this severe condition.

Compulsive buying: Earlier illicit drug use, impulse buying, depression, and adult ADHD symptoms.

PubMed

Brook, Judith S; Zhang, Chenshu; Brook, David W; Leukefeld, Carl G

2015-08-30

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant's earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Compulsive Buying: Earlier Illicit Drug Use, Impulse Buying, Depression, and Adult ADHD Symptoms

PubMed Central

Brook, Judith S.; Zhang, Chenshu; Brook, David W.; Leukefeld, Carl G.

2015-01-01

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant’s earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. PMID:26165963

Self-reported mental health among US military personnel prior and subsequent to the terrorist attacks of September 11, 2001.

PubMed

Smith, Tyler C; Smith, Besa; Corbeil, Thomas E; Riddle, James R; Ryan, Margaret A K

2004-08-01

There is much concern over the potential for short- and long-term adverse mental health effects caused by the terrorist attacks on September 11, 2001. This analysis used data from the Millennium Cohort Study to identify subgroups of US military members who enrolled in the cohort and reported their mental health status before the traumatic events of September 11 and soon after September 11. While adjusting for confounding, multivariable logistic regression, analysis of variance, and multivariate ordinal, or polychotomous logistic regression were used to compare 18 self-reported mental health measures in US military members who enrolled in the cohort before September 11, 2001 with those military personnel who enrolled after September 11, 2001. In contrast to studies of other populations, military respondents reported fewer mental health problems in the months immediately after September 11, 2001.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].

PubMed

Vanegas, Jairo; Vásquez, Fabián

Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Extraversion and taste sensitivity.

PubMed

Zverev, Yuriy; Mipando, Mwapatsa

2008-03-01

The rationale for investigating the gustatory reactivity as influenced by personality dimensions was suggested by some prior findings of an association between extraversion and acuity in other sensory systems. Detection thresholds for sweet, salty, and bitter qualities of taste were measured in 60 young healthy male and female volunteers using a two-alternative forced-choice technique. Personality of the responders was assessed using the Eysenck Personality Inventory. Multivariate analysis of variance failed to demonstrate a statistically significant interaction between an extraversion-introversion score, neuroticism score, smoking, gender and age. The only reliable negative association was found between the body mass index (BMI) and taste sensitivity (Roy's largest root = 0.05, F(7436.5) = 8.34, P = 0.003). Possible reasons for lack of differences between introverts and extraverts in the values of taste detection thresholds were discussed.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.